09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable software and operating system software for
safety, inspections, monitoring, testing, compliance,
certification, validation, abstraction, error detection and
correction, foundations, calibrating, controlling, training,
system behavior adjustments, system isolations, critical
system checks, safety logic, alerting, accessing and
interpreting camera data, perception and inspection systems,
simulation, system and environment modeling, hypervisors,
contingency planning, and system guardrails; downloadable
software for autonomous and semi-autonomous systems, smart
spaces, and machines, namely, software providing guardrails,
rules, logic, operational functionality, sensors, and
compliance; downloadable software for inspection,
compliance, training, and behavior modification of hardware,
compute platforms, high performance computers, and GPUs;
downloadable software comprising middleware, sensor, and
abstraction layers, schedulers, communication libraries, and
system frameworks for use in autonomous applications;
downloadable software in the nature of full stack platforms
for autonomous operations consisting of comprehensive safety
systems and compliance, infrastructure, operating system
software, and middleware; downloadable software for unifying
edge computing into an integrated safety platform;
downloadable software for implementing functional safety
protocols in autonomous and semi-autonomous systems and
smart spaces; downloadable software for real-time artificial
intelligence inference at the network edge; downloadable
software for managing and orchestrating safety-critical
computing workloads; downloadable software for monitoring,
diagnosing, and ensuring compliance with functional safety
standards in computing systems; downloadable software for
integrating safety-certified computing functions with
artificial intelligence decision-making in real-time
applications; downloadable software for developing,
deploying, and managing safety-critical artificial
intelligence applications at the network edge; downloadable
software for real-time monitoring and enforcement of
functional safety standards in artificial intelligence
computing systems; downloadable software for training,
simulating, and validating autonomous and semi-autonomous
systems and smart spaces at scale; downloadable software for
providing a safety evaluation framework for autonomous and
semi-autonomous systems and smart spaces; downloadable
software for building and documenting safety cases for
autonomous and semi-autonomous systems and smart spaces;
downloadable software incorporating tools and guidelines for
evaluating functional safety of autonomous and
semi-autonomous systems and smart spaces; downloadable
software development kits (SDKs) for use in autonomous
applications; downloadable machine learning software for
enabling computers to learn to perform tasks autonomously;
downloadable computer software using machine learning for
developing predictive models; downloadable and recorded
computer programs for monitoring the performance of computer
systems for robotic, autonomous and semi-autonomous, and
smart space systems; downloadable software for programming
physical movements for use in robots; downloadable operating
system software for robots; downloadable software for
machine learning for use in robots; recorded software and
operating system software for safety, inspections,
monitoring, testing, compliance, certification, validation,
abstraction, error detection and correction, foundations,
calibrating, controlling, training, system behavior
adjustments, system isolations, critical system checks,
safety logic, alerting, accessing and interpreting camera
data, perception and inspection systems, simulation, system
and environment modeling, hypervisors, contingency planning,
and system guardrails; recorded software for autonomous and
semi-autonomous systems, smart spaces, and machines, namely,
software providing guardrails, rules, logic, operational
functionality, sensors, and compliance; recorded software
for inspection, compliance, training, and behavior
modification of hardware, compute platforms, high
performance computers, and GPUs; recorded software
comprising middleware, sensor, and abstraction layers,
schedulers, communication libraries, and system frameworks
for use in autonomous applications; recorded software in the
nature of full stack platforms for autonomous operations
consisting of comprehensive safety systems and compliance,
infrastructure, operating system software, and middleware;
recorded software for unifying edge computing into an
integrated safety platform; recorded software for
implementing functional safety protocols in autonomous and
semi-autonomous systems and smart spaces; recorded software
for real-time artificial intelligence inference at the
network edge; recorded software for managing and
orchestrating safety-critical computing workloads; recorded
software for monitoring, diagnosing, and ensuring compliance
with functional safety standards in computing systems;
recorded software for integrating safety-certified computing
functions with artificial intelligence decision-making in
real-time applications; recorded software for developing,
deploying, and managing safety-critical artificial
intelligence applications at the network edge; recorded
software for real-time monitoring and enforcement of
functional safety standards in artificial intelligence
computing systems; recorded software for training,
simulating, and validating autonomous and semi-autonomous
systems and smart spaces at scale; recorded software for
providing a safety evaluation framework for autonomous and
semi-autonomous systems and smart spaces; recorded software
for building and documenting safety cases for autonomous and
semi-autonomous systems and smart spaces; recorded software
incorporating tools and guidelines for evaluating functional
safety of autonomous and semi-autonomous systems and smart
spaces; recorded software development kits (SDKs) for use in
autonomous applications; recorded machine learning software
for enabling computers to learn to perform tasks
autonomously; recorded computer software using machine
learning for developing predictive models; recorded software
for programming physical movements for use in robots;
recorded operating system software for robots; recorded
software for machine learning for use in robots. Providing online non-downloadable software and operating
system software for safety, inspections, monitoring,
testing, compliance, certification, validation, abstraction,
error detection and correction, foundations, calibrating,
controlling, training, system behavior adjustments, system
isolations, critical system checks, safety logic, alerting,
accessing and interpreting camera data, perception and
inspection systems, simulation, system and environment
modeling, hypervisors, contingency planning, and system
guardrails; providing online non-downloadable software for
autonomous and semi-autonomous systems, smart spaces, and
machines, namely, software for providing guardrails, rules,
logic, operational functionality, sensors, and compliance;
providing online non-downloadable software for inspection,
compliance, training, and behavior modification of hardware,
compute platforms, high performance computers, and GPUs;
providing online non-downloadable software comprising
middleware, sensor, and abstraction layers, schedulers,
communication libraries, and system frameworks for use in
autonomous applications; providing online non-downloadable
software using artificial intelligence agents and modules
for monitoring, controlling, testing, training, controlling,
moving, and mobilizing autonomous machines; providing online
non-downloadable software in the nature of full stack
platforms for autonomous operations consisting of
comprehensive safety systems and compliance, infrastructure,
operating system software, and middleware; providing online
non-downloadable software for unifying edge computing into
an integrated safety platform; providing online
non-downloadable software for implementing functional safety
protocols in autonomous and semi-autonomous systems and
smart spaces; providing online non-downloadable software for
real-time artificial intelligence inference at the network
edge; providing online non-downloadable software for
managing and orchestrating safety-critical computing
workloads; providing online non-downloadable software for
monitoring, diagnosing, and ensuring compliance with
functional safety standards in computing systems; providing
online non-downloadable software for integrating
safety-certified computing functions with artificial
intelligence decision-making in real-time applications;
providing online non-downloadable software for developing,
deploying, and managing safety-critical artificial
intelligence applications at the network edge; providing
online non-downloadable software for real-time monitoring
and enforcement of functional safety standards in artificial
intelligence computing systems; providing online
non-downloadable software for training, simulating, and
validating autonomous and semi-autonomous systems and smart
spaces at scale; providing online non-downloadable software
for providing a safety evaluation framework for autonomous
and semi-autonomous systems and smart spaces from operator
assistance to autonomous operation; providing online
non-downloadable software for building and documenting
safety cases for autonomous and semi-autonomous systems and
smart spaces; providing online non-downloadable software
incorporating tools and guidelines for evaluating functional
safety of autonomous and semi-autonomous systems and smart
spaces; providing online non-downloadable software
development kits (SDKs) for use in autonomous applications;
technology consultation in the field of artificial
intelligence (AI); providing information in the fields of
technology and software development for autonomous systems
and robotics via an on-line website; providing information
relating to computer technology and programming for robotic,
autonomous and semi-autonomous, and smart space systems via
a website.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable computer software, namely, prebuilt, optimized
software containers and inference microservices for
deploying, running, integrating, scaling, and managing
artificial intelligence and machine learning models;
downloadable electronic data files featuring source code,
text, and images for deploying, running, integrating,
scaling, and managing artificial intelligence and machine
learning models. Providing temporary use of online non-downloadable software,
namely, prebuilt, optimized inference microservices for
deploying, running, integrating, scaling, and managing
artificial intelligence and machine learning models;
providing online non-downloadable API software for
artificial intelligence model inference.
09 - Scientific and electric apparatus and instruments
Goods & Services
Downloadable electronic data files featuring physical
characteristics and operational parameters of robots for use
in robotics research and simulation; downloadable data sets
for developing, training, testing, and evaluating artificial
intelligence models for use in robotics simulation,
calibration, and system identification, namely, downloadable
data sets featuring robot experiment telemetry, motion data,
system-identification measurements, and operational
parameters of robots for use in robotics research, robot
simulation, simulation calibration, validation of
physics-engine, actuator, and sensor models, and evaluation
for robotics applications; downloadable data sets for the
analyzing and reviewing of data featuring robot experiment
telemetry, motion data, system-identification measurements,
and operational parameters of robots for use in robotics
research, robot simulation, simulation calibration,
validation of physics-engine, actuator, and sensor models,
and evaluation for robotics applications.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable computer software for training, deploying, and
running artificial intelligence and machine learning models
for robotics, autonomous vehicles, and vision AI agents;
downloadable computer software development kits (SDKs) for
creating, testing, and fine tuning world models and world
foundation models; downloadable computer software libraries
for synthetic data generation and processing of sensor data
for training and evaluating physical AI models; downloadable
machine learning (ML) models, foundation models, and
pretrained neural network (NN) models, all for physical AI
development in the fields of robotics, autonomous vehicles,
and vision AI agents; downloadable electronic data files
featuring source code, text, and images, all for physical AI
development in the fields of robotics, autonomous vehicles,
and vision AI agents. Providing temporary use of non-downloadable computer
software for training, deploying, and running artificial
intelligence and machine learning models for robotics,
autonomous vehicles, and vision AI agents; providing
temporary use of non-downloadable computer software for
creating, testing, and fine tuning world models and world
foundation models; providing temporary use of
non-downloadable computer software for synthetic data
generation and processing of sensor data for physical AI
development; software as a service (SaaS) featuring software
for training, deploying, and running artificial intelligence
and machine learning models for robotics, autonomous
vehicles, and vision AI agents; artificial intelligence as a
service (AIaaS) services featuring software for training,
deploying, and running artificial intelligence and machine
learning models for robotics, autonomous vehicles, and
vision AI agents; software as a service (SaaS) featuring
software for creating, testing, and fine tuning world models
and world foundation models; artificial intelligence as a
service (AIaaS) services featuring software for creating,
testing, and fine tuning world models and world foundation
models; software as a service (SaaS) featuring software for
synthetic data generation and processing of sensor data for
physical AI development; artificial intelligence as a
service (AIaaS) services featuring software for synthetic
data generation and processing of sensor data for physical
AI development.
Devices and methods are disclosed for power conversion using hybrid-switched capacitor converter (HSCC) devices. In at least one embodiment, an HSCC device includes a first power train, a second power train, and a coupling device. The first power train includes a first output terminal. The second power train includes a second output terminal. The coupling device includes, a first input terminal, a second input terminal and, a third output terminal. The first input terminal of the coupling device is connected to the first output terminal, and the second input terminal of the coupling device is connected to the second output terminal. Current from the first and second power trains induces coupling in the coupling device, enhancing performance across different states and various duty cycles.
H02M 3/07 - Conversion of DC power input into DC power output without intermediate conversion into AC by static converters using resistors or capacitors, e.g. potential divider using capacitors charged and discharged alternately by semiconductor devices with control electrode
6.
POWER DISTRIBUTION ARCHITECTURE FOR GPU ACCELERATOR ARRAY SYSTEMS
Power supply and distribution architectures, e.g., for supplying and distributing power to a GPU accelerator array of a data center server. In at least one embodiment, a power supply and distribution architecture includes a power distribution board having both a power converter for converting a supply voltage of a power supply to an operating voltage of a GPU and a GPU power connector for transmitting power to the GPU at the operating voltage.
Apparatuses, systems, and techniques to identify a first one or more fifth generation new radio (5G NR) signal pairs having one or more quality characteristics based, at least in part, on neural network weight information received from a plurality of different sources. In at least one embodiment, neural network weight information can be aggregated to generate one or more neural networks to identify the first one or more 5G NR signal pairs.
Systems and methods are disclosed related to collaborative texture filtering. In at least one embodiment, in a graphics processing unit (GPU) executes a plurality of processing threads organized into waves. In at least one embodiment, redundant texel fetches are identified and one or more threads are reassigned to fetch unique texels through collaboration.
Apparatuses, systems, and techniques related to computing intensity images from infrared (IR) wavelength data and visibility wavelength data. In at least one embodiment, each IR image corresponding to the IR wavelength data is fused with a luminance (luma) image computed from corresponding the visibility wavelength data to produce IR/luma intensity images.
Apparatuses, systems, and techniques related to a perception neural network model determining controls for an image signal processor (ISP). In at least one embodiment, the perception neural network model extracts attributes and semantic features from an image to produce perception data. The perception data is used to determine one or more ISP controls to enhance a portion of the image.
G06T 5/60 - Image enhancement or restoration using machine learning, e.g. neural networks
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
11.
VARIANCE AWARE TECHNIQUES TO PRUNE NEURAL NETWORKS
Systems and methods to use weight information corresponding to a neural network, wherein weight information of at least a first one or more layers of the neural network is modified to compensate for deactivating one or more second layers of the neural network. The neural network may be a residual neural network.
In various examples, apparatuses, systems, software, and methods are described for selecting decoders to decode messages from encoders in a wireless network. For example, a base station includes a processor that uses a machine learning model to select a decoder from a plurality of decoders to decode an encoded representation of information received from an encoder in a user equipment (UE) device.
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
In various examples, one or more neural networks are to be performed by a number of processors. The neural network may be reconfigured during inferencing such that the number of processors that start the inferencing is different from the number of processors that finish the inferencing.
In various examples, methods for training machine learning models to predict a binding affinity between molecules in a biological complex using supervised and unsupervised learning techniques includes generating an unlabeled training data set based on random perturbation applied to biological complexes included in a labeled training data set, wherein: the labeled training data set includes a plurality of samples of biological complexes, and each respective sample of a biological complex is associated with a calculated binding affinity between molecules in the respective sample; training a predictive model to predict binding affinity of a first molecule to a second molecule based on a first loss associated with the labeled training data set and a second loss associated with the unlabeled training data set; and deploying the trained predictive model.
Systems and methods are disclosed that extracting material information using one or more video diffusion models. For example, embodiments of the present disclosure may process one or more images and a text prompt using a first video diffusion model to generate a rendered video of an object. For instance, the one or more images may be based on a three-dimensional (3D) geometry of the object and lighting conditions and the rendered image may include frames that show different viewpoints of the object. Subsequently, a differentiable renderer may be used to process the rendered video to generate a material data structure for the object, which indicates material information for points from the 3D geometry of the object. The material information may indicate physical-based rendering (PBR) characteristics such as an albedo, roughness, and/or metallic characteristic of the points on the object.
Apparatuses, systems, and techniques to implement cooperative thread groups. In at least one embodiment, two or more groups of synchronized threads are synchronized and executed in parallel for efficient execution on a GPU.
Devices, systems, and techniques for assessing and managing security risk scenarios associated with an artificial intelligence (AI) model executed by a primary computing system. The techniques include generating, based on a set of security-related data associated with an AI model, a library comprising a set of identified ai security risk scenarios associated with the AI model. The techniques include classifying, based on a set of AI model criteria, a first identified security risk scenario of the set of identified security risk scenarios into a first category of a set of categories to generate a first classified security risk scenario. The techniques include causing a simulated security attack associated with the computing system to be created based on the first classified security risk scenario.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
18.
SURFACE FEATURE DETECTION FROM POINT CLOUDS FOR AUTONOMOUS AND SEMI-AUTONOMOUS SYSTEMS AND APPLICATIONS
In various examples, surface features such as lane lines or road boundaries may be detected and 2D or 3D polylines representing their shape may be regressed. For example, a set of predefined vertical, slanted, and/or curved anchors may be used to represent corresponding lines (e.g., in a top-down view or bird's eye view). For each anchor, extracted features may be sampled along the anchor and processed using one or more DNNs to generate global (e.g., anchor-level) predictions such as an anchor-level existence or confidence score and/or local (e.g., per-point) predictions for each of a set of sampled points along the anchor such as regressed polylines and per-point classifications. The detected surface features may be used by one or more control components such as a drive or control stack for localization, safe planning, and control of an autonomous vehicle, semi-autonomous vehicle, machine, robot, and/or other object or machine type.
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
19.
DENOISING IMAGES OF DYNAMICALLY DISPLACED PARAMETRIC SURFACES
In various examples, images of geometrically refined parametric surfaces may be denoised using motion vectors computed in 2D screen space and/or using locations of corresponding control points. Surfaces in a scene may be geometrically refined (e.g., subdivided or tessellated), an image of the scene may be ray traced or rasterized, and motion vectors may be computed in 2D screen space and/or using corresponding control points on the control cages for geometrically refined parametric surfaces. In some embodiments with displaced surfaces, motion vectors are computed by comparing 3D locations on a rendered (displaced) surface or by comparing corresponding 3D locations on the limit surface, and displacement may be determined using the applicable displacement function (e.g., a displacement map, texture, procedural math function, some combination thereof) or by measuring or deriving it from approximated locations on the displaced and limit surfaces. As such, the image may be denoised using the motion vectors.
Disclosed are apparatuses, systems, and techniques that train and use trained language models to assist users with complex systems installation, troubleshooting, and/or maintenance. The techniques include receiving, via a user interface (UI), a natural language (NL) query associated with one or more malfunction indicators indicative of a malfunction state of a system, providing, to a language model (LM) trained using a documentation associated with the system, an input having a prompt that is based at least on the NL query. The techniques further include receiving, from the LM, a response to the NL query, the response having one or more instructions associated with resolution of the malfunction state of the system and causing the UI to display the response.
Apparatuses, systems, and techniques are presented to generate panoramas from individual images. In at least one embodiment, one or more generative neural networks are used to generate a spherical panoramic image using features extracted from a single input image.
Various examples, systems, and methods are disclosed relating to orthogonal triangle strips. A first computing system can execute one or more operations to obtain an approximation of a three-dimensional (3D) curve including a plurality of linear segments. The first computing system can execute one or more operations to determine two quadrilateral primitives (quads) for each of the plurality of linear segments using a plurality of triangle primitives, wherein each of the two quads includes two of the plurality of triangle primitives, wherein the two quads corresponding to each linear segment of the plurality of linear segments are angled relative to one another and extend along the corresponding linear segment. The first computing system can execute one or more operations to render the two quads for each of the plurality of linear segments.
Apparatuses, systems, and techniques to provisioning of pipelines for efficient training, retraining, configuring, deploying, and using machine learning models for inference in user-specific platforms.
Approaches presented herein relate to active messaging for parallel processing architectures, such as utilizing active messages (AMs) for direct graphics processing unit (GPU)-to-GPU communications. Gadgets may be launched across multiple GPUs for specific types of message handling. When a gadget is launched, the gadget may run across multiple GPUs to send messages between the different GPUs handling a given task. In operation, a gadget may include multiple handlers, and may be executed for specific tasks through completion prior to shutting down. In this manner, multiple gadgets may be launched sequentially to execute different selected operations across the GPUs.
In various examples, apparatuses, systems, software, and methods are described for executing a Riding Peaks algorithm in parallel using multiple threads (e.g., GPU threads) to allocate sub-bands to user equipment device (UE)s. Examples of UEs include mobile phones, in-machine infotainment devices, smart devices, security systems, and/or other wireless-capable devices. For example, a processor executing an algorithm that allocates sub-bands to UEs by using threads (e.g., GPU threads) operating concurrently to identify peaks and assign sub-bands.
In various examples, towing- or generally external object transportation-detection for autonomous and/or semi-autonomous systems and applications is described herein. Systems and methods are disclosed that use one or more sensors to automatically determine when a machine is towing an object. For instance, sensor data obtained using the sensor(s)-such as image data, LiDAR data, RADAR data, ultrasonic data, and/or the like-may be used to determine information related to the object and/or the machine. This information may then be used to determine that the machine is towing the object, such as when the object remains a substantially similar distance from the machine when moving and/or when the object includes one or more specific types of classifications. The systems of methods may then perform one or more operations.
In various examples, a modular (e.g., control, drive, software, software/hardware) stack may evaluate candidate trajectory(ies) by predicting the subsequent scene or world state that would result from using the candidate trajectory(ies), and quantifying a measure of the quality, safety, and/or favorability of the predicted subsequent state. If the quality, safety, and/or favorability of the estimated subsequent state satisfies a designated threshold (e.g., the candidate trajectory is predicted to result in a favorable, safe, desirable, and/or beneficial future state), the candidate trajectory may be used to control navigation. If not, a determination may be made to disengage autonomous navigation, execute a safety procedure, and/or a parameterized optimization may be used to generate an optimized trajectory using an optimized planner to iteratively refine a trajectory predicted to lead to the most desirable state—or at least a state beyond a threshold desirability.
One embodiment of a method for determining object poses includes receiving first sensor data and second sensor data, where the first sensor data is associated with a first modality, and the second sensor data is associated with a second modality that is different from the first modality, and performing one or more iterative operations to determine a pose of an object based on one or more comparisons of (i) one or more renderings of a three-dimensional (3D) representation of the object in the first modality with the first sensor data, and (ii) one or more renderings of the 3D representation of the object in the second modality with the second sensor data.
Apparatuses, systems, and techniques are presented to compress data. In at least one embodiment, one or more images are generated based, at least in part, upon one or more compressed images and image enhancement data generated using one or more neural networks.
Apparatuses, systems, and techniques to perform matrix multiply-acumulate operations with block scaling of specified portions of input matrices by scale factors. In at least one embodiment, an input matrix is multiplied by a scaled matrix that is generated by multiplying elements of a second input matrix by one or more corresponding scale factors stored in tensor memory.
Apparatuses, systems, and techniques to perform matrix multiply-accumulate operations with block scaling of specified portions of input matrices by scale factors. In at least one embodiment, an input matrix is multiplied by a scaled matrix that is generated by multiplying elements of a second input matrix by one or more corresponding scale factors stored in one or more registers.
Apparatuses, systems, and techniques are presented to generate image or video content representing at least one point of view. In at least one embodiment, one or more neural networks are used to generate one or more images of one or more objects from a first point of view based at least in part upon one or more images of the one or more objects from a second point of view.
Apparatuses, systems, and techniques to generate complete depictions of objects based on a partial depiction of the object. In at least one embodiment, an image of a complete object is generated by one or more neural networks, based on an image of a portion of the object, using an encoder of the one or more neural networks trained using training data generated from output of a decoder of the one or more neural networks.
In various examples, systems and methods related to depth-assisted 3D detection are provided. The techniques described herein may be used to monitor a state of a vehicle occupant with high precision using depth-assisted 3D detection. Sensors may be used to capture image data and depth data that includes features of the vehicle occupant (e.g., eyes, head, hands, etc.). The 2D image coordinates from the image data may be converted to 3D coordinates in a global coordinate system using depth data and a 3D occupant model, which may be personalized to the vehicle occupant. Precise values may be determined based on the 3D coordinates in a global coordinate system, and the state of features of the vehicle occupant may be estimated based on these values. The values and/or state of features of the vehicle occupant may be used to detect drowsiness, distraction, and/or other conditions and provide safe vehicle operation.
In various examples, machine learning model agent-based analysis of debugging reports for circuit design systems and applications is disclosed. A multi-file report analysis agent may analyze the original structure of individual timing reports to construct a structured report database for the set of reports. Based on the individual reports incorporated into the structured report database, the report analysis agent may construct a TDRG that connects the individual reports based on relationships. An MCMM planner agent may decompose the user-provided evaluation task inputs into sub-tasks for multiple corners and modes and call a TDRG traversal agent to resolve individual sub-tasks, as indicated by a mode plan. The TDRG traversal agent prompts expert report agents to retrieve information from the structural report database based on traversing the TDRG. An expert report agent may generate data retrieval code that may be executed to query the specified report from the structured report database.
G06F 30/3315 - Design verification, e.g. functional simulation or model checking using static timing analysis [STA]
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
36.
SYSTEMS AND METHODS FOR ROOT CAUSE FAILURE IDENTIFICATION USING MULTI-STAGE RETRIEVAL-AUGMENTED GENERATION
In various examples, systems and methods for root cause failure identification using multi-stage retrieval-augmented generation are provided. A multi-stage retrieval-augmented generation system may include first model(s) that may extract a first context from test log(s) including a test failure based on first prompt(s) and information retrieved from first database(s). The multi-stage retrieval-augmented generation system may also include second model(s) that may extract a second context from product log(s) corresponding to the test log(s) based on the first context extracted from the test log(s), second prompt(s), and information retrieved from second database(s). Operation(s) for addressing the test failure may be performed based on the second context extracted from the product log(s) includes generating a report that identifies a root cause of at least one test failure, generating proposed change(s) to a test script for the automated testing, and/or generating proposed change(s) to source code for the product under test.
A system includes a first cooling loop to cool one or more first components of a server and exchange first heat with an ambient environment via a first heat exchanger. The first cooling loop can exchange heat directly with the ambient environment without a cooling distribution unit.
Apparatuses, systems, and techniques to facilitate storage allocation. In at least one embodiment, an application programming interface is used to execute instructions to cause storage to be allocated based on an indication of whether the storage may be modified.
Apparatuses, processors, computing systems, devices, non-transitory computer medium, and/or methods for using one or more neural networks to identify a number of wireless devices communicating with one or more base stations based, at least in part, on communication signals, data structures, or information received by one or more base stations. For example, a processor is to use one or more neural networks to identify a number of wireless devices communicating with one or more base stations based, at least in part, on communication signals received by the one or more base stations.
H04W 74/0833 - Random access procedures, e.g. with 4-step access
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
Apparatuses, systems, and techniques are presented schedule operations to be performed. In at least one embodiment, two or more data partitions of a neural network are caused to be automatically scheduled to be performed by two or more corresponding logical processors in parallel.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer hardware; computer software; downloadable software
development kits (SDKs); downloadable and recorded
artificial intelligence models and large language models
(LLMs), namely pre-trained neural network models for
generating, processing, understanding and analyzing natural
language, speech, text, images, video, audio, software code
and structured and unstructured data, including generative
artificial intelligence models, multimodal models, computer
vision models, speech and audio models, recommendation
models, embedding models and other machine learning and
foundation models; downloadable and recorded computer
software featuring artificial intelligence, machine learning
and deep learning; downloadable and recorded computer
software for the collection, transmission, management,
analyzing, reviewing and displaying of data, text, images,
audio, video and multimedia content; downloadable and
recorded computer software for developing, running and
analyzing algorithms that are able to learn to analyze,
classify, and take actions in response to exposure to data;
downloadable and recorded computer software for developing,
training, fine-tuning, testing, evaluating, deploying and
monitoring artificial intelligence models, large language
models (LLMs) and other machine learning and generative
artificial intelligence models, including multimodal,
computer vision, speech, recommendation and embedding
models; downloadable and recorded computer software for use
in generating and executing automated and autonomous
processes and tasks; downloadable and recorded computer
software for generating and executing autonomous tasks in
response to exposure to data using machine learning and
artificial intelligence; downloadable and recorded computer
software that uses artificial intelligence and machine
learning to generate, process, understand and analyze
speech, text, images, videos, sounds, software code and
tasks; downloadable and recorded software for facilitating
interaction, communication, and actions between humans and
artificial intelligence chatbots and agents and between
artificial intelligence chatbots and agents; downloadable
and recorded computer software for developing, customizing,
and managing computer systems, databases, mobile and
computer software applications, client interfaces,
frameworks, and templates; downloadable and recorded
computer software for use as an autonomous, on-machine agent
that automates and executes tasks using artificial
intelligence; downloadable and recorded computer software
for facilitating interaction with third-party application
programming interfaces (APIs) and platforms; downloadable
and recorded computer software, namely, customizable
software toolkits that enable extensibility through
user-defined functions, and support API requests,
deployments, and other tasks; downloadable and recorded
computer software for executing commands using a shell and
for integration with and deployment of large language models
(LLMs) and other artificial intelligence models, including
generative artificial intelligence models, multimodal
models, computer vision models, speech and audio models,
recommendation engines, embedding models and other machine
learning and foundation models; downloadable and recorded
computer software for using artificial intelligence for
configuring, generating, and running interactive
simulations; downloadable and recorded computer software for
creating simulations using natural language, images, video,
and audio data; downloadable electronic publications in the
nature of technical documentation, model cards, white
papers, instruction manuals, user guides and reference
manuals in the field of artificial intelligence, machine
learning, large language models (LLMs), generative
artificial intelligence models, multimodal models and
related software platforms; downloadable chatbot software
for simulating conversations, analyzing images, sound and
video, summarizing text, creating content, generating code,
brainstorming, trip planning, and answering queries;
downloadable computer software for facilitating interaction
and communication between humans and artificial intelligence
(AI) chatbots in the fields of artificial intelligence,
machine learning, natural language generation, statistical
learning, mathematical learning, supervised learning, and
unsupervised learning; downloadable chatbot software for
providing information from searchable indexes and databases
of information, including text, images, videos, software
algorithms, mathematical equations, electronic documents,
and databases; computer hardware and recorded software for
the autonomous driving of motor vehicles; downloadable
software in the nature of vehicle operating system software;
downloadable software for autonomous vehicle operation,
navigation, steering, calibration, and management; computer
hardware and recorded software for use with vehicle cameras;
downloadable computer programs and downloadable software for
artificial intelligence (AI), machine learning, and deep
learning for use in connection with operating autonomous
vehicles, systems, devices; recorded software and computer
hardware for use in connection with and for operating
autonomous vehicles, systems, devices; computer hardware and
recorded software for operating vehicle cameras; computer
hardware for operating autonomous vehicles. Providing temporary use of non-downloadable computer
software; providing online software platforms; software as a
service (SaaS); artificial intelligence as a service
(AIAAS); software as a service (SaaS) featuring computer
software platforms for building, training, fine-tuning,
testing, evaluating, deploying, scaling and monitoring
artificial intelligence models and large language models
(LLMs) and other machine learning and generative artificial
intelligence models, including multimodal models, computer
vision models, speech and audio models, recommendation
models, embedding models and other foundation models;
software as a service (SaaS) and providing temporary use of
non-downloadable computer software for developing, training,
fine-tuning, testing, evaluating, deploying and monitoring
artificial intelligence models, large language models (LLMs)
and other machine learning and generative artificial
intelligence models, including multimodal, computer vision,
speech, recommendation and embedding models; software as a
service (SaaS) and providing temporary use of
non-downloadable computer software for the collection,
transmission, management, analyzing, reviewing and
displaying of data, text, images, audio, video and
multimedia content; software as a service (SaaS) and
providing temporary use of non-downloadable computer
software for developing, running and analyzing algorithms
that are able to learn to analyze, classify, and take
actions in response to exposure to data; software as a
service (SaaS) and providing temporary use of
non-downloadable computer software for use in generating and
executing automated and autonomous processes and tasks;
software as a service (SaaS) and providing temporary use of
non-downloadable computer software for generating and
executing autonomous tasks in response to exposure to data
using machine learning and artificial intelligence; software
as a service (SaaS) and providing temporary use of
non-downloadable computer software that uses artificial
intelligence and machine learning to generate, process,
understand and analyze speech, text, images, videos, sounds,
software code and tasks; software as a service (SaaS) and
providing temporary use of non-downloadable computer
software for facilitating interaction, communication, and
actions between humans and artificial intelligence chatbots
and agents and between artificial intelligence chatbots and
agents; software as a service (SaaS) and providing temporary
use of non-downloadable computer software for developing,
customizing, and managing computer systems, databases,
mobile and computer software applications, client
interfaces, frameworks, and templates; software as a service
(SaaS) and providing temporary use of non-downloadable
computer software as an autonomous, on-machine agent that
automates and executes tasks using artificial intelligence;
software as a service (SaaS) and providing temporary use of
non-downloadable computer software for facilitating
interaction with third-party APIs and platforms; software as
a service (SaaS) and providing temporary use of
non-downloadable open-source computer software, namely,
customizable software toolkits that enable extensibility
through user-defined functions, and supporting API requests,
deployments, and other tasks; providing temporary use of
non-downloadable artificial intelligence models and large
language models (LLMs) via online interfaces and application
programming interfaces (APIs) for generating, processing,
understanding and analyzing natural language, speech, text,
images, video, audio, software code and structured and
unstructured data, including via generative artificial
intelligence models, multimodal models, computer vision
models, speech and audio models, recommendation models,
embedding models and other machine learning and foundation
models; software as a service (SaaS) and providing temporary
use of non-downloadable computer software for executing
commands using a shell and for integration with and
deployment of large language models (LLMs) and other
artificial intelligence models, including generative
artificial intelligence models, multimodal models, computer
vision models, speech and audio models, recommendation
engines, embedding models and other machine learning and
foundation models; software as a service (SaaS) and
providing temporary use of non-downloadable computer
software for using artificial intelligence for configuring,
generating, and running interactive simulations; software as
a service (SaaS) and providing temporary use of
non-downloadable computer software for creating simulations
using natural language, images, video, and audio data;
providing technical information relating to computer
software, namely, artificial intelligence, machine learning,
and software development via a website; providing computer
software consulting services; technical support services,
namely, troubleshooting in the nature of diagnosing and
repairing computer software problems; research, development
and evaluation of large language models and data sets;
research, design and development of computer programs and
software; software as a service (SaaS) and providing
temporary use of non-downloadable chatbot software for
simulating conversations, analyzing images, sound and video,
summarizing text, creating content, generating code,
brainstorming, trip planning, and answering queries;
software as a service (SaaS) and providing temporary use of
non-downloadable computer software for facilitating
interaction and communication between humans and artificial
intelligence (AI) chatbots in the fields of artificial
intelligence, machine learning, natural language generation,
statistical learning, mathematical learning, supervised
learning, and unsupervised learning; software as a service
(SaaS) and providing temporary use of non-downloadable
chatbot software for providing information from searchable
indexes and databases of information, including text,
images, videos, software algorithms, mathematical equations,
electronic documents, and databases; Artificial intelligence
as a service (AIAAS) services featuring software using
artificial intelligence for developing data science models;
Artificial intelligence as a service (AIAAS) services
featuring software using artificial intelligence for
creating and integrating computer models; providing online
non-downloadable software for the autonomous driving of
motor vehicles; providing online non-downloadable software
for autonomous vehicle navigation, steering, calibration,
and management; providing online non-downloadable software
used for data analytics in the field of transportation;
providing temporary use of non-downloadable data sets in the
field of machine perception and autonomous driving
technology; providing information about autonomous-vehicle
and machine-perception research via a website; research,
design, and development in the field of artificial
intelligence; research, design, and development in the field
of autonomous technology; research, design, and development
of vehicle software; technological, scientific and research
services in the field of robotics, self-driving car and
autonomous vehicle technology; providing virtual computer
systems and environments through cloud computing for the
purpose of training self-driving cars, autonomous vehicles
and robots.
42.
DISTANCE DETERMINATIONS USING ONE OR MORE NEURAL NETWORKS
Apparatuses, systems, and techniques are presented to determine distance for one or more objects. In at least one embodiment, a disparity network is trained to determine distance data from input stereoscopic images using a loss function that includes at least one of a gradient loss term and an occlusion loss term.
Apparatuses, systems, and techniques to allocate one or more compute resources to a user device. In at least one embodiment, one or more circuits cause one or more compute resources to be allocated to two or more fifth-generation (5G) radio access network (RAN) cells based, at least in part, on interference between the two or more 5G RAN cells.
Apparatuses, systems, and techniques are to transfer data based, at least in part, on a computational graph. In at least one embodiment, a processor causes a compiler to generate instructions to prefetch one or more data values from dynamic random access memory (DRAM) into an in-processor cache based, at least in part, on a computational graph.
G06F 12/0862 - Addressing of a memory level in which the access to the desired data or data block requires associative addressing means, e.g. caches with prefetch
G06F 12/1072 - Decentralised address translation, e.g. in distributed shared memory systems
45.
CACHING OF COMPILED SHADER PROGRAMS IN A CLOUD COMPUTING ENVIRONMENT
Apparatuses, systems, and techniques for caching compiled shader programs in a cloud computing environment. A request for a compiled shader program associated with an initial rendering operation is received from a client device. Based on the request, at least one of a shader key representing a hardware or software state of the client device, or a shader value derived from source code of the compiled shader program is obtained. The shader key or shader value is provided as input to a trained model. From the trained model, an indication of additional compiled shader programs and corresponding confidence levels that each will be requested within a time window is obtained. The compiled shader program and additional compiled shader programs having confidence levels satisfying confidence criteria are retrieved from a shader cache and provided to the client device.
Apparatuses, systems, and techniques to execute CUDA programs. In at least one embodiment, an application programming interface is performed to determine a scheduling policy of one or more blocks of one or more threads.
One embodiment of a method for controlling a robot includes performing a plurality of simulations of a robot interacting with one or more objects represented by one or more signed distance functions (SDFs), where performing the plurality of simulations comprises reducing a number of contacts between the one or more objects that are being simulated, and updating one or more parameters of a machine learning model based on the plurality of simulations to generate a trained machine learning model.
In various examples, a visualization of an environment may be generated using a Panini projection that is optimized based on detected scene content. For example, image data of an environment may be perspective projected (e.g., using a rectilinear projection) to generate a reference projection image, which may be analyzed to detect the presence of vanishing points and/or horizontal lines (e.g., in a central region). The image data of the environment may be projected using a Panini projection that is optimized based on distances to detected objects, the absence of a detected vanishing point, and/or the presence of a detected horizontal line to generate a Panini projection image. In some embodiments, vertical compression is applied to the Panini projection image to correct for distortion of horizontal lines (e.g., based on the presence of a detected horizontal line).
In various examples, datasets for training computer vision and/or other artificial intelligence (AI) models may be automatically generated using AI-based techniques. For instance, the systems and methods of the present disclosure may crawl network resources (e.g., webpages) that include information associated with an interactive application (e.g., a wiki page for a gaming application) to obtain “seed” images depicting objects from the interactive application, as well as information (e.g., metadata) corresponding to the seed images. In some examples, a multimodal language model may be used to process the seed images and corresponding information. Based on the processing, the systems and methods may assign one or more labels to the seed images. The labeled seed images may then be used to expand the training dataset by processing and identifying additional images that depict similar objects to those in the labeled seed images.
G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
Neural networks, and in particular deep neural networks, are traditionally resource intensive as they require execution of a significant number of computations in order to generate an output. In an effort to accelerate neural networks, quantization has been employed in which the input/output of certain operations are scaled. However, the degree to which a neural network can be quantized is limited because current scaling methods, which do not ensure consistent scaling for operands, cannot be applied to the commonly used element-wise operations (e.g. addition and subtraction). The present disclosure provides scaling of element-wise operations in a neural network by using scale factors that are shared between operands, thus allowing for greater quantization of neural networks.
Apparatuses, systems, and techniques to edit data elements of a spherical image. In at least one embodiment, two or more software threads are used to perform two or more fast Fourier transforms (FFTs) on two or more pixels of one or more spherical images.
Apparatuses, systems, methods, and techniques to process a work request (e.g., a Work Queue Element (WQE)) using graphics processing unit (GPU) resources. In at least one embodiment, the GPU detects one or more work requests to be processed, retrieves the work request(s), and processes the work request(s) in parallel.
In various examples, techniques for neural rendering of characters using attention masks are described herein. Systems and methods described herein may use various techniques with regard to neural rendering (and/or any other rendering techniques) to render smooth, stable, and/or quality interactive characters. For instance, in some examples, an attention mask may be used to stabilize the motion outside of a specific region of a character-such as a mouth region of the character-that may be caused by audio inputs processed using one or more neural networks. Additionally, in some examples, an attention mask may be used to stabilize motion outside of other specific regions of the character-such as eye regions of the character-that may be caused by eye movement inputs processed using the neural network(s). As such, the masks may be used to control the motion that is caused by the different inputs.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer hardware; downloadable software; downloadable
software for system safety, cybersecurity, inspections,
monitoring, testing, compliance, certification, validation,
abstraction, data management, workflows, inter-process
communication, scheduling, error detection and correction,
foundations, calibrating, controlling, training, system
behavior adjustments, system isolations, critical system
checks, system communications, hardware integration,
artificial intelligence (AI) perception, safety logic,
accessing and interpreting camera data, hypervisors,
contingency planning, and system guardrails; downloadable
software for operating system safety, cybersecurity,
inspections, monitoring, testing, compliance, certification,
validation, abstraction, data management, workflows,
inter-process communication, scheduling, error detection and
correction, foundations, calibrating, controlling, training,
system behavior adjustments, system isolations, critical
system checks, system communications, hardware integration,
artificial intelligence (AI) perception, safety logic,
accessing and interpreting camera data, hypervisors,
contingency planning, and system guardrails; downloadable
software for autonomous systems and machines, namely,
software providing guardrails, rules, logic, operational
functionality, scheduling, monitoring, controls,
communications, sensors, testing, and compliance;
downloadable software for integration, monitoring,
inspection, compliance, control, scheduling, training, and
behavior modification of hardware, compute platforms, high
performance computers, computer processors, GPUs, machines,
robots, autonomous systems and machines, and autonomous
vehicles; downloadable software comprising middleware,
sensor, and abstraction layers, schedulers, communication
libraries, system frameworks for use in autonomous
applications; downloadable software using artificial
intelligence agents and modules for monitoring, controlling,
testing, training, moving, and mobilizing autonomous
machines, robots, robotaxis, and autonomous vehicles;
downloadable software in the nature of full stack platforms
for autonomous operations consisting of comprehensive safety
systems, monitoring, and compliance, infrastructure,
operating system software, middleware, software
applications; downloadable software for unifying real-time
artificial intelligence processing, functional safety
monitoring, and edge computing into an integrated safety
platform; downloadable software for implementing functional
safety protocols in autonomous and semi-autonomous systems;
downloadable software for real-time artificial intelligence
inference at the network edge; downloadable software for
managing and orchestrating safety-critical computing
workloads; downloadable software for monitoring, diagnosing,
and ensuring compliance with functional safety standards in
computing systems; downloadable software for integrating
safety-certified computing functions with artificial
intelligence decision-making in real-time applications;
downloadable software for developing, deploying, and
managing safety-critical artificial intelligence
applications at the network edge; downloadable software for
real-time monitoring and enforcement of functional safety
standards in artificial intelligence computing systems;
downloadable software for training, simulating, and
validating autonomous vehicle systems at scale; downloadable
software for providing a safety evaluation framework for
autonomous driving systems from driver assistance to
autonomous operation; downloadable software for building and
documenting safety cases for autonomous and semi-autonomous
vehicle systems; downloadable software for cloud-based
development infrastructure enabling autonomous vehicle
training, simulation, and validation; downloadable software
for real-time artificial intelligence inference in
autonomous vehicles; downloadable software incorporating
tools and guidelines for evaluating functional safety of
autonomous driving systems; recorded software; preinstalled
software; downloadable software development kits (SDKs);
downloadable software development tools; downloadable
software featuring libraries for software development;
downloadable artificial intelligence software; downloadable
software for facilitating data transmission, data
collection, data analysis, and decision-making; downloadable
software for simulation, modeling and data processing for
use in data visualization, data analysis, data mining, data
interpretation, predictive analytics, accessing and editing
large-scale data; downloadable software for use in general
purpose computation, manipulation of collections of data,
data analysis, image content analysis, information analysis,
data transformation, data input/output, communications,
speech recognition, graphics display, modeling and testing
for use in the fields of artificial intelligence, deep
learning, high performance computing, distributed computing,
virtualization and machine learning; downloadable software
for data management, analytics and pattern and activity
recognition; downloadable software for artificial
intelligence, machine learning, deep learning, statistical
learning, supervised learning, unsupervised learning, data
mining, predictive analytics, business intelligence, and
computer vision; downloadable software, namely,
knowledge-based artificial intelligence platforms, data
analytics platforms, and automation platforms; downloadable
software for deploying, distributing, configuring, testing,
installing, upgrading, updating, customizing, debugging, and
managing other software; downloadable software for data and
application migration; downloadable electronic data files
featuring source code, text, and images; downloadable
electronic publications; downloadable computer software
using machine learning for creating data models;
downloadable machine learning software for enabling
computers to learn to perform tasks autonomously;
downloadable computer software using machine learning for
developing predictive models; downloadable and recorded
computer programs for monitoring the performance of computer
systems; downloadable operating system software for robots;
downloadable software for machine learning for use in
robots; downloadable application programming interfaces
(APIs); downloadable computer software libraries;
downloadable data sets for generating, processing,
understanding and analyzing natural language, speech, text,
images, video, audio, software code and structured and
unstructured data, namely, data sets for training, testing,
validating, evaluating, and monitoring artificial
intelligence models for autonomous vehicles, robots,
autonomous machines, and safety-critical physical AI
systems, including generative artificial intelligence
models, multimodal models, computer vision models, speech
and audio models, recommendation models, embedding models
and other machine learning and foundation models;
downloadable data sets for generating and executing
autonomous tasks in response to exposure to data using
machine learning and artificial intelligence for training,
testing, validating, evaluating, and monitoring artificial
intelligence models for autonomous vehicles, robots,
autonomous machines, and safety-critical physical AI
systems; downloadable data set for the collection,
transmission, management, analyzing, reviewing and
displaying of data, text, images, audio, video and
multimedia content for training, testing, validating,
evaluating, and monitoring artificial intelligence models
for autonomous vehicles, robots, autonomous machines, and
safety-critical physical AI systems; downloadable data sets
for developing, training, fine-tuning, testing, evaluating,
deploying and monitoring artificial intelligence models,
large language models (LLMs) and other machine learning and
generative artificial intelligence models, including
multimodal, computer vision, speech, recommendation and
embedding models for autonomous vehicles, robots, autonomous
machines, and safety-critical physical AI systems. Providing online non-downloadable software; providing online
non-downloadable software for system safety, cybersecurity,
inspections, monitoring, testing, compliance, certification,
validation, abstraction, data management, workflows,
inter-process communication, scheduling, error detection and
correction, foundations, calibrating, controlling, training,
system behavior adjustments, system isolations, critical
system checks, system communications, hardware integration,
artificial intelligence (AI) perception, safety logic,
accessing and interpreting camera data, hypervisors,
contingency planning, and system guardrails; providing
online non-downloadable software for operating system
safety, cybersecurity, inspections, monitoring, testing,
compliance, certification, validation, abstraction, data
management, workflows, inter-process communication,
scheduling, error detection and correction, foundations,
calibrating, controlling, training, system behavior
adjustments, system isolations, critical system checks,
system communications, hardware integration, artificial
intelligence (AI) perception, safety logic, accessing and
interpreting camera data, hypervisors, contingency planning,
and system guardrails; providing online non-downloadable
software for autonomous systems and machines, namely,
software for providing guardrails, rules, logic, operational
functionality, scheduling, monitoring, controls,
communications, sensors, testing, and compliance; providing
online non-downloadable software for integration,
monitoring, inspection, compliance, control, scheduling,
training, and behavior modification of hardware, compute
platforms, high performance computers, computer processors,
GPUs, machines, robots, autonomous systems and machines, and
autonomous vehicles; providing online non-downloadable
software comprising middleware, sensor, and abstraction
layers, schedulers, communication libraries, system
frameworks for use in autonomous applications; providing
online non-downloadable software using artificial
intelligence agents and modules for monitoring, controlling,
testing, training, moving, and mobilizing autonomous
machines, robots, robotaxis, and autonomous vehicles;
providing online non-downloadable software for unifying
real-time artificial intelligence processing, functional
safety monitoring, and edge computing into an integrated
safety platform; providing online non-downloadable software
for implementing functional safety protocols in autonomous
and semi-autonomous systems; providing online
non-downloadable software for real-time artificial
intelligence inference at the network edge; providing online
non-downloadable software for managing and orchestrating
safety-critical computing workloads; providing online
non-downloadable software for monitoring, diagnosing, and
ensuring compliance with functional safety standards in
computing systems; providing online non-downloadable
software for integrating safety-certified computing
functions with artificial intelligence decision-making in
real-time applications; providing online non-downloadable
software for developing, deploying, and managing
safety-critical artificial intelligence applications at the
network edge; providing online non-downloadable software for
real-time monitoring and enforcement of functional safety
standards in artificial intelligence computing systems;
providing online non-downloadable software for training,
simulating, and validating autonomous vehicle systems at
scale; providing online non-downloadable software for
providing a safety evaluation framework for autonomous
driving systems from driver assistance to autonomous
operation; providing online non-downloadable software for
building and documenting safety cases for autonomous and
semi-autonomous vehicle systems; providing online
non-downloadable software for cloud-based development
infrastructure enabling autonomous vehicle training,
simulation, and validation; providing online
non-downloadable software for real-time artificial
intelligence inference in autonomous vehicles; providing
online non-downloadable software incorporating tools and
guidelines for evaluating functional safety of autonomous
driving systems; software as a service (SaaS); cloud
computing services featuring software for use in artificial
intelligence monitoring, compliance, safety, training,
control, behavior modification, and assessment; providing
online non-downloadable software development kits (SDKs);
providing online non-downloadable software development
tools; providing online non-downloadable software featuring
libraries for software development; providing online
non-downloadable artificial intelligence software; providing
online non-downloadable software for facilitating data
transmission, data collection, data analysis, and
decision-making; providing online non-downloadable software
for simulation, modeling and data processing for use in data
visualization, data analysis, data mining, data
interpretation, predictive analytics, accessing and editing
large-scale data, interactive visual computing, design of
information graphics, and maximizing graphics processing and
performance; providing online non-downloadable software for
use in general purpose computation, manipulation of
collections of data, data analysis, image content analysis,
information analysis, data transformation, data
input/output, communications, speech recognition, graphics
display, modeling and testing for use in the fields of
artificial intelligence, deep learning, high performance
computing, distributed computing, virtualization and machine
learning; providing online non-downloadable software for
data management, analytics and pattern and activity
recognition; providing online non-downloadable software for
artificial intelligence, machine learning, deep learning,
natural language generation, statistical learning,
supervised learning, unsupervised learning, data mining,
predictive analytics, business intelligence, and computer
vision; providing online non-downloadable software, namely,
knowledge-based artificial intelligence platforms, data
analytics platforms, and automation platforms; providing
online non-downloadable software for enhancing computer
performance and for operation of integrated circuits,
semiconductors, computer chipsets, micro-processors, GPUs,
and dpus; providing online non-downloadable software for
deploying, distributing, configuring, testing, installing,
upgrading, updating, customizing, debugging, and managing
other software; providing online non-downloadable software
for data and application migration; computer software
technical support services, technical information and
technical support regarding software patches, upgrades and
updates; advanced product research in the field of
artificial intelligence; providing information relating to
computer technology; providing information relating to
computer programming; providing information relating to
computer programs; providing computer hardware and software
information online; platform as a service (PaaS) services;
infrastructure as a service (IaaS); artificial intelligence
as a service (AIaaS) services; hosting of computer
platforms; artificial intelligence as a service (AIaaS)
services featuring software using artificial intelligence
for integrating computer models; research in the field of
autonomous car technology; technology consultation in the
field of artificial intelligence (AI); application service
provider (ASP), namely, hosting computer software
applications of others; scientific and technological
services and research and design relating thereto; quality
control and authentication services; providing information
in the fields of technology and software development via an
on-line website; providing information relating to computer
technology and programming via a website.
55.
DECODING CODEWORDS ENCODED USING LINEAR BLOCK CODES
Apparatuses, systems, and techniques to decode codewords encoded using linear block codes. In at least one embodiment, a codeword encoded using a linear block code is decoded by at least using one or more interleavers to generate a plurality of permutations based, at least in part, on automorphisms of the linear block code and the codeword, wherein at least one interleaver of the one or more interleavers is to be re-used multiple times as part of generating the plurality of permutations.
H03M 13/27 - Coding, decoding or code conversion, for error detection or error correctionCoding theory basic assumptionsCoding boundsError probability evaluation methodsChannel modelsSimulation or testing of codes using interleaving techniques
H03M 13/37 - Decoding methods or techniques, not specific to the particular type of coding provided for in groups
56.
VIDEO DIFFUSION MODELS WITH TEMPORALLY CONSISTENT NOISE
Two and three dimensional video diffusion models configured via training to be equivariant to spatial transformations of the input by training the model using warped noise, where equivariance of the models is learned using a conventional video diffusion loss function modified to replace independent noise inputs with warped noise inputs.
Systems and methods for a datacenter cooling system are disclosed. In at least one embodiment, a secondary cooling loop interfaces with a primary cooling loop and includes at least one processor that is adapted with a first proportional-integral-derivative (PID) controller to control a first flow controller, which can cause a flow rate for a secondary coolant from a coolant distribution unit (CDU) to a plurality of second flow controllers, and where the plurality of second flow controllers are associated with a plurality of second PID controllers and the flow rate is based in part of on feedback from the plurality of second PID controllers.
A system for manufacturing an object includes a non-transitory storage device and a processor coupled to the non-transitory storage device. The processor is configured to determine a value of an attribute of a first component of the object; determine, using a machine learning model and based on the value, a manufacturing parameter for performing a manufacturing operation with respect to a second component that is functionally interrelated with the first component; and provide instructions for manufacturing the object by performing the manufacturing operation according to the manufacturing parameter to generate the second component.
G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
In various examples, systems and method are provided for generation of ground truth gaze data for training in-cabin monitoring systems and applications. A gaze target projector mounted to a known position inside a cabin may be used to project a gaze target onto an interior surface of the cabin. Because a beam of light may be used to produce the projected gaze target, the projected gaze target may be displayed at a projection point on the surface of the cabin interior, even if the surface at the projection point is curved, small, or an irregular shape. Three-dimensional coordinates of a projected gaze target in the cabin coordinate system may be determined and used to label image data that is captured as a projected gaze target is selectively projected onto an interior surface of the cabin and a test occupant's gaze is directed at the projected gaze target.
G06V 20/59 - Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
B60W 40/08 - Estimation or calculation of driving parameters for road vehicle drive control systems not related to the control of a particular sub-unit related to drivers or passengers
G06F 3/01 - Input arrangements or combined input and output arrangements for interaction between user and computer
Methods and systems for obtaining data having a first format, converting the data to a second format, storing the converted data in memory accessible by at least one parallel processing unit, and processing the converted data stored in the memory using the at least one parallel processing unit.
Apparatuses, systems, and techniques to train neural networks to perform image processing tasks. In at least one embodiment, one or more second neural networks are used to train one or more first neural networks based, at least in part, on a first object type in one or more images and a second object type in the one or more images, in parallel.
A device includes memory to store a first session key and a second session key different from the first session key. A transmitter (TX) physical layer is to transmit frames to a second device over a link and a receiver (RX) physical layer is to receive frames from the second device over the link. A TX datalink logic is coupled to the TX physical layer and the memory, the TX datalink logic to cause encryption of frame data transmitted to the second device using the first session key. A RX datalink logic is coupled to the RX physical layer and the memory, the RX datalink logic to cause decryption of frame data received from the second device using the second session key.
In various examples, metadata-based image harmonization for image stitching systems and applications are disclosed. Systems and methods are disclosed that preprocess images with respect to rendering parameters, with the effect of blending those parameters at a border between images to facilitate a smooth rendering when those images are stitched together. An image signal processing (ISP) parameter harmonization function may input metadata parameters associated with a set of images to match and blend one or more of the rendering parameters across an overlapping border between images prior to applying those images to a stitching algorithm. A scaling of the metadata parameter may be performed using a parameter gain function. Pixels in both images located along the border are adjusted to the same boundary metadata parameter value, and smoothed based on the parameter gain function. A discontinuity in rendering parameters is avoided, substantially avoiding corresponding artifacts in the resulting stitched image.
This specification describes techniques for implementing matrix multiply and add (MMA) operations in graphics processing units (GPU)s and other processors. The implementations provide for a plurality of warps of threads to collaborate in generating the result matrix by enabling each thread to share its respective register files to be accessed by the datapaths associated with other threads in the group of warps. A state machine circuit controls a MMA execution among the warps executing on asynchronous computation units. A group MMA (GMMA) instruction provides for a descriptor to be provided as parameter where the descriptor may include information regarding size and formats of input data to be loaded into shared memory and/or the datapath.
G06F 9/30 - Arrangements for executing machine instructions, e.g. instruction decode
G06F 7/544 - Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using non-contact-making devices, e.g. tube, solid state deviceMethods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using unspecified devices for evaluating functions by calculation
G06F 9/38 - Concurrent instruction execution, e.g. pipeline or look ahead
Approaches presented herein provide systems and methods for generating three-dimensional (3D) objects using compressed data as an input. One or more models may learn from a hash table of latent features to map different features to a reconstruction domain, using a hash function as part of a learned process. A 3D shape for an object may be encoded to a multi-layered grid and represented by a series of embeddings, where given point within the grid May be interpolated based on the embeddings for a given layer of the multi-layered grid. A decoder may then be trained to use the embeddings to generate an output object.
In various examples, a technique for generating speed change decisions for a mobile robot includes identifying, using one or more maps of a physical environment, one or more obstacles associated with one or more portions of a path of the mobile robot in the physical environment. The technique also includes generating, based at least on the one or more obstacles, one or more speed constraints, each speed constraint specifying a speed limit for a respective portion of the path. The technique further includes generating one or more speed change decisions specifying actions to be performed by the mobile robot to cause a speed profile of the mobile robot to satisfy the one or more speed constraints.
Technology related to calibrating synchronization of a data signal to a forwarded clock signal is described. A circuit includes a receiver circuit and a clock-to-data synchronization circuit. The receiver circuit receives a data signal and includes two inverters and two resistors. The clock-to-data synchronization circuit receives the data signal and the clock signal and determines an indication of synchronization between the data and clock signals. The clock-to-data synchronization circuit adjusts, using the indication, a parameter of the receiver circuit to synchronize the data signal and the clock signal.
The disclosed method for generating a first depth map for a first frame of a video includes performing one or more operations to generate a first intermediate depth map based on the first frame and a second frame preceding the first frame within the video, performing one or more operations to generate a second intermediate depth map based on the first frame, and performing one or more operations to combine the first intermediate depth map and the second intermediate depth map to generate the first depth map.
G06V 10/762 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
69.
TEMPORAL IMAGE BLENDING USING ONE OR MORE NEURAL NETWORKS
Apparatuses, systems, and techniques are presented to reconstruct one or more images. In at least one embodiment, one or more objects in an image are caused to be generated based, at least in part, on applying one or more offsets to a motion of the one or more objects relative to one or more prior images.
In various examples, Artificial Intelligence (AI)-based systems and methods may be used to autonomously manage the movement of items in a variety of environments, such as a retail space. For instance, the systems and methods of the present disclosure may (e.g., using one or more AI models) continuously update a virtual representation (e.g., digital twin) of an environment based on images depicting the environment. The virtual representation of the environment may be used to track locations of various objects in real time. In some examples, the systems of the present disclosure may use the virtual representation of the environment to select optimal machines for moving items, select optimal paths for the machines to follow, etc. Additionally, the systems of the present disclosure may use 3D models of items in the virtual representation to determine grasps and/or control commands for manipulators of the machines to pick up the items.
G05B 19/4155 - Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by programme execution, i.e. part programme or machine function execution, e.g. selection of a programme
G06F 30/13 - Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
G06T 11/60 - Editing figures and textCombining figures or text
71.
SOFTWARE-ASSISTED HARDWARE CACHE FOR TEXTURE DECOMPRESSION
In computer graphics, texture refers to a type of surface, including the material characteristics, that can be applied to an object in an image. In some implementations, a texture may be represented as an image that can be placed on a three-dimensional (3D) model of an object to give surface details to the 3D object. To reduce a size of textures (e.g. for storage and transmission), textures may be compressed. However, current decompression methods that execute in software shader code are costly, especially where high compression ratios are involved. The present disclosure provides for a software-assisted hardware cache for texture decompression, which can be used to access already decompressed textures thereby reducing a number of decompressions required to be performed.
In various examples, Artificial Intelligence (AI)-based systems and methods may be used to autonomously manage intellectual property (IP)-related submissions. For example, a system(s) may obtain project-related information from one or more sources and use one or more AI models (e.g., a multimodal AI model(s)) to automatically generate a document representative of an invention submission. In some instances, the AI model(s) may identify missing details related to the invention submission and perform one or more operations to obtain those missing details. For instance, the AI model(s) may search through additional sources of information and/or initiate conversations with relevant individuals (e.g., inventors) to collect the missing information. Additionally, in some examples, the system(s) may automatically escalate certain tasks and take proactive actions, such as initiating a review or approving an invention submission responsive to learning that a public disclosure is approaching.
H04L 51/02 - User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
73.
SYSTEMS AND METHODS OF CONTEXTUAL FILTERING FOR VLM PROCESSING
Various examples, systems, and methods are disclosed relating to contextual filtering for vision language model (VLM) processing. A system can detect a motion between a first frame of image data and a second frame of image data. A system can determine a first similarity score between the first frame and the second frame and a second similarity score between the second frame and a context for processing of the second frame. A system can generate a combined score. A system can process the second frame using one or more neural networks responsive to determining that one or more frame selection criteria are satisfied based at least on the combined score.
Apparatuses, systems, and techniques for generating a binary to be deployed to multiple processors. In at least one embodiment, a compiler is to generate a binary file comprising instructions to be performed on one or more processors, wherein the instructions included in the binary file are generated from a set of intermediate representation instructions limited to those having compatibility with a classification of processors indicated to the compiler.
In various examples, techniques for neural rendering of characters using operating states are described herein. Systems and methods described herein may use various techniques with regard to neural rendering (and/or any other rendering techniques) to render smooth, stable, and/or quality interactive characters. For instance, a presentation of a character may operate in different operating states, such as an idle state where the character is not interacting, a transition state where the character is preparing to interact, and an interactive state where the character is interacting. To maintain a smooth transition between the operating states, poses associated with the character may be maintained, even when transitioning switches from recorded frames to newly generated frames. In some examples, the idle state and the transition state may use the recorded frames while the interactive state uses the newly generated frames to improve the performance of the presentation.
Bi-directional optical communication modules, cables, computing systems, and interconnect modules are provided. An example module includes a substrate supporting a first optical transmitter configured to generate a first optical signal at a first wavelength, a first optical receiver configured to receive a second optical signal at a second wavelength distinct from the first wavelength, and a band pass filter communicably coupled to the first optical transmitter, the first optical receiver, and an optical port. The band pass filter passes the first optical signal to the optical port and directs the second optical signal from the optical port into the first optical receiver, enabling simultaneous bi-directional transmission over a single optical fiber between modules coupled to host devices.
Apparatuses, systems, and techniques including application programming interfaces (APIs), compilers, and instructions to allocate a number of registers per thread, to deallocate a number of allocated registers for a thread, and to allocate a number of registers per function. For example, a processor performs an API to indicate a number of registers to allocate to a thread when performing matrix multiplication operations on a graphics processing unit (GPU). As another example, a processor performs an API to indicate a number of registers to allocate to a function.
Various embodiments enable a robot, or other autonomous or semi-autonomous device or system, to receive data involving the performance of a task in the physical world. The data can be provided as input to a perception network to infer a set of percepts about the task, which can correspond to relationships between objects observed during the performance. The percepts can be provided as input to a plan generation network, which can infer a set of actions as part of a plan. Each action can correspond to one of the observed relationships. The plan can be reviewed and any corrections made, either manually or through another demonstration of the task. Once the plan is verified as correct, the plan (and any related data) can be provided as input to an execution network that can infer instructions to cause the robot, and/or another robot, to perform the task.
G05B 13/00 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
Apparatuses, systems, and techniques to perform an application programming interface (API) to add one or more graph nodes to a software graph, wherein the API is to cause a child graph node to be added to a software graph based, at least in part, on a dependency type indicated by the API. In at least one embodiment, one or more nodes are added to a graph in accordance to one or more dependency types.
Apparatuses, systems, and methods are to cause an image denoising filter to identify noise within an image to be performed. In at least one embodiment, an image denoising filter identifies noise within an image noise based, at least in part, on whether a plurality of concurrent comparisons of pixel depths within the image exceed a threshold value.
Apparatuses, systems, and techniques of identifying one or more distances to one or more objects. In at least one embodiment, one or more distances to one or more objects are identified based, at least in part on one or more first optical rays of a first image depicting an objects and one or more second optical rays a second images depicting the object. In at least one embodiment, one or more machine learning processes (e.g., neural network(s)) are used to identify a correspondence ray to determine distances to one or more objects.
H04N 13/232 - Image signal generators using stereoscopic image cameras using a single 2D image sensor using fly-eye lenses, e.g. arrangements of circular lenses
H04N 13/268 - Image signal generators with monoscopic-to-stereoscopic image conversion based on depth image-based rendering [DIBR]
82.
Determination of luminance values using image signal processing pipeline
An imaging system comprises a camera and one or more processors. The one or more processors are to process a plurality of channels associated with color information in a color space of an image captured by the camera to generate a second plurality of channels associated with corrected color information in a corrected color space. The one or more processors are further to determine a plurality of luminance values based at least in part on the second plurality of channels and an exposure value of the camera, and generate an updated version of the image using the plurality of luminance values.
H04N 9/797 - Processing of colour television signals in connection with recording for recording the signal in a plurality of channels, the bandwidth of each channel being less than the bandwidth of the signal
Apparatuses, systems, and techniques are presented to generate images. In at least one embodiment, ray tracing is caused to be selectively performed on one or more objects within a three-dimensional (3D) environment.
Apparatuses, systems, and techniques to facilitate selective use of one or more texture interpolation operations. In at least one embodiment, one or more circuits are to perform an application programming interface (API) to selectively enable one or more texture interpolation operations.
Disclosed are apparatuses, systems, and techniques that improve efficiency and quality of data streaming in time-sensitive network communications. The techniques include but are not limited to proactive replacement of packets in network communications that use forward error correction techniques. Proactive replacement of packets that have been lost or can potentially become lost reduces network latency and increases the number of timely communicated data messages.
H04L 1/00 - Arrangements for detecting or preventing errors in the information received
H04L 1/16 - Arrangements for detecting or preventing errors in the information received by using return channel in which the return channel carries supervisory signals, e.g. repetition request signals
In various examples, an environment surrounding an ego-object is visualized using an adaptive 3D bowl that models the environment with a shape that changes based on distance (and direction) to one or more representative point(s) on detected objects. Distance (and direction) to detected objects may be determined using 3D object detection or a top-down 2D or 3D occupancy grid, and used to adapt the shape of the adaptive 3D bowl in various ways (e.g., by sizing its ground plane to fit within the distance to the closest detected object, fitting a shape using an optimization algorithm). The adaptive 3D bowl may be enabled or disabled during each time slice (e.g., based on ego-speed), and the 3D bowl for each time slice may be used to render a visualization of the environment (e.g., a top-down projection image, a textured 3D bowl, and/or a rendered view thereof).
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestriansRecognition of traffic objects, e.g. traffic signs, traffic lights or roads
H04N 5/262 - Studio circuits, e.g. for mixing, switching-over, change of character of image, other special effects
88.
BOUNDING AREA PLANNING USING A CONGESTION PREDICTION MODEL
Apparatuses, systems, and techniques for bounding area planning using a congestion prediction model. Placement data associated with cells of an IC design is identified. A graph based at least on the identified placement data is generated. The graph is provided as input to a machine learning model. The machine learning model is trained to predict, based at least on a given graph associated with cells according to a respective IC design, a congestion level for cells at one or more bounding areas of a respective IC design. One or more outputs of the machine learning model are obtained. The one or more outputs include congestion data indicating a congestion level for a first bounding area of the IC design. Cells are caused to be placed within at least one region, of the IC design, corresponding to the first bounding area.
Apparatuses, systems, and techniques to cause information to be invalidated in a second cache location after information is stored in a first cache location. In at least one embodiment, one or more circuits are to perform an application programming interface (API) to cause information to be invalidated in a second cache location after information is stored in a first cache location.
G06F 12/0811 - Multiuser, multiprocessor or multiprocessing cache systems with multilevel cache hierarchies
G06F 12/0804 - Addressing of a memory level in which the access to the desired data or data block requires associative addressing means, e.g. caches with main memory updating
G06F 12/0891 - Addressing of a memory level in which the access to the desired data or data block requires associative addressing means, e.g. caches using clearing, invalidating or resetting means
90.
APPLICATION PROGRAMMING INTERFACE TO SHARE STORAGE
Apparatuses, systems, and techniques to share portions of storage across operating system instances. In at least one embodiment, one or more APIs are performed to cause a portion of storage used by a first operation system instance to be shared with a second operation system instance using a handle identifying the portion of storage.
In various examples, a conversational artificial intelligence (AI) platform uses structured data and unstructured data to generate responses to queries from users. In an example, if data for a response to a query is not stored in a structured data structured, the conversational AI platform searches for the data in an unstructured data structure.
G06F 16/38 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
G10L 13/08 - Text analysis or generation of parameters for speech synthesis out of text, e.g. grapheme to phoneme translation, prosody generation or stress or intonation determination
Apparatuses, systems, and techniques to blend two or more images based on confidence values of objects within said two or more images. In at least one embodiment, one or more confidence values in one or more images are generated using one or more neural networks that are used, for example, to blend two or more images to be displayed.
Embodiments of the present disclosure relate to real-time neural appearance models. Using a neural decoder, scenes are rendered in real-time with complex material appearance previously reserved for offline use. Learned hierarchical textures representing the material properties are encoded as latent codes. When a ray is cast and intersects with geometry in the scene, the intersection point is mapped to one of the latent codes. The latent code is interpreted using neural decoders, which produce reflectance values and importance-sampled directions that can be used to determine a pixel color.
In accordance with the disclosure, an inductor may be formed over a semiconductor substrate of one or both dies in a face-to-face die arrangement while reducing the parasitic capacitance between the inductor and the adjacent die. In disclosed embodiments, a semiconductor device may include a void (e.g., an air gap) between the inductor and the adjacent die to reduce the parasitic capacitance between the inductor and the adjacent die. The void may be formed in the die that includes the inductor and/or the adjacent die. In some respects, the void may be etched in interface layers (e.g., comprising bump pads and dielectric material) between the semiconductor dies, and may extend along the length of the inductor.
A system is described having one or more processing devices that prepare a work queue entry (WQE), transmit the WQE or provide a doorbell indication indicating the WQE is available, and, in response to determining an execution failure has occurred, perform at least one additional step to facilitate completion of an operation associated with the WQE.
In various examples, a three-dimensional (3D) intersection structure may be predicted using a deep neural network (DNN) based on processing two-dimensional (2D) input data. To train the DNN to accurately predict 3D intersection structures from 2D inputs, the DNN may be trained using a first loss function that compares 3D outputs of the DNN—after conversion to 2D space—to 2D ground truth data and a second loss function that analyzes the 3D predictions of the DNN in view of one or more geometric constraints—e.g., geometric knowledge of intersections may be used to penalize predictions of the DNN that do not align with known intersection and/or road structure geometries. As such, live perception of an autonomous or semi-autonomous vehicle may be used by the DNN to detect 3D locations of intersection structures from 2D inputs.
G06T 7/33 - Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
Embodiments of the present disclosure are directed to the proactive re-routing of network traffic upon a failure of a link or node of the network in a manner that reduces overall performance degradation. As described herein, embodiments use a host or other node of the network to detect and react to the failure instead of a switch since the host or node can do so more quickly than the switch. Once the host or node detects a failure, that host or node can cause communications between the nodes to switch from one node, such as packet spray routing, for example, to another node, such as Equal Cost Multi-Path (ECMP) routing, for example. Packets intended for the failed node can be re-routed to other nodes and the communications can be returned to the original mode.
Apparatuses, systems, and techniques to identify one or more decoding techniques to be used based, at least in part, on one or more requests from one or more users. In at least one embodiment, a processor comprises processing circuitry to identify one or more decoding techniques to generate one or more responses to one or more requests based, at least in part, on one or more constraints, and cause one or more computing resources to perform the one or more decoding techniques to generate the one or more responses to the one or more requests.
A mesh that includes a virtual object to be rendered for display by a client device is generated by execution of software that lacks access to a rendering pipeline of the client device and based at least in part on a first pose generated by a client device. The mesh is caused to be rendered according to a second pose by providing the mesh and color data to the rendering pipeline via an application programming interface call.
In various examples, systems and methods are disclosed relating to managing confidential containers implementing graphics processing unit (GPU) workloads are disclosed. A system can initialize a virtual machine corresponding to a confidential computing environment. The system can generate a mapping for at least one physical component interface to the virtual machine. The system can update a configuration setting(s) of a container to be executed within the virtual machine based on the mapping and a container-device interface (CDI). The system can execute the container within the confidential computing environment of the virtual machine according to the configuration setting(s).
