Apparatuses, systems, and techniques to cause one or more neural networks to be trained. In at least one embodiment, a processor includes one or more circuits to cause one or more neural networks to be trained based, at least in part, on one or more capabilities.
H04L 41/16 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p.ex. des réseaux de commutation de paquets en utilisant l'apprentissage automatique ou l'intelligence artificielle
2.
PARALLEL WORKLOAD SCHEDULING BASED ON WORKLOAD DATA COHERENCE
Approaches for addressing issues associated with processing workloads that exhibit high divergence in execution and data access are provided. A plurality of workload items to be processed at least partially in parallel may be identified. Coherence information associated with the plurality of workload items may be determined. The plurality of workload items may be enqueued in a segmented queue. The plurality of workload items may be sorted based at least on a similarity of the coherence information. The sorted plurality of workload items may be stored to the queue. Using a set of processing units, the workload items in the queue may be processed at least partially in parallel according to an order of the sorting.
Techniques applicable to a ray tracing hardware accelerator for traversing a hierarchical acceleration structure with reduced false positive ray intersections are disclosed. The reduction of false positives may be based upon one or more of selectively performing a secondary higher precision intersection test for a bounding volume, identifying and culling bounding volumes that degenerate to a point, and parametrically clipping rays that exceed certain configured distance thresholds.
Apparatuses, systems, and techniques to perform versions of program code. In at least one embodiment, one or more versions of a plurality of versions of software code are performed. In at least one embodiment, one or more versions of a plurality of versions of software code are performed based, at least in part, on whether the versions of the program code access overlapping memory regions.
Disclosed are apparatuses, systems, and techniques that may use machine learning for determining transmitted signals in communication systems that deploy orthogonal frequency division multiplexing. A system for performing the disclosed techniques includes receiving (RX) antennas to receive RX signals, each RX signal received over a respective resource element of a resource grid. Individual resource elements of the resource grid are associated with different radio subcarriers and/or data symbols. The RX signals include a combination of a plurality of transmitted (TX) streams. The system further includes a processing device to process the RX signals using one or more neural network models to determine TX data symbols transmitted via the plurality of TX streams.
Systems and methods are disclosed that relate to freespace detection using machine learning models. First data that may include object labels may be obtained from a first sensor and freespace may be identified using the first data and the object labels. The first data may be annotated to include freespace labels that correspond to freespace within an operational environment. Freespace annotated data may be generated by combining the one or more freespace labels with second data obtained from a second sensor, with the freespace annotated data corresponding to a viewable area in the operational environment. The viewable area may be determined by tracing one or more rays from the second sensor within the field of view of the second sensor relative to the first data. The freespace annotated data may be input into a machine learning model to train the machine learning model to detect freespace using the second data.
G06V 20/56 - Contexte ou environnement de l’image à l’extérieur d’un véhicule à partir de capteurs embarqués
G01S 13/89 - Radar ou systèmes analogues, spécialement adaptés pour des applications spécifiques pour la cartographie ou la représentation
G01S 17/89 - Systèmes lidar, spécialement adaptés pour des applications spécifiques pour la cartographie ou l'imagerie
G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
7.
REDUCING FALSE POSITIVE RAY TRAVERSAL IN A BOUNDING VOLUME HIERARCHY
Techniques applicable to a ray tracing hardware accelerator for traversing a hierarchical acceleration structure with reduced false positive ray intersections are disclosed. The reduction of false positives may be based upon one or more of selectively performing a secondary higher precision intersection test for a bounding volume, identifying and culling bounding volumes that degenerate to a point, and parametrically clipping rays that exceed certain configured distance thresholds.
Apparatuses, systems, and techniques to process image frames. In at least one embodiment, one or more neural networks are used to blend two or more video frames between a first video frame and a second video frame. In at least one embodiment, a blended video frame is used to generate an intermediate video frame between the first video frame and the second video frame.
G06T 7/579 - Récupération de la profondeur ou de la forme à partir de plusieurs images à partir du mouvement
G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
H04N 19/132 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par l’élément, le paramètre ou la sélection affectés ou contrôlés par le codage adaptatif Échantillonnage, masquage ou troncature d’unités de codage, p.ex. ré-échantillonnage adaptatif, saut de trames, interpolation de trames ou masquage de coefficients haute fréquence de transformée
Apparatuses, systems, and techniques to select one or more beams to transmit signals. In at least one embodiment, a system includes one or more circuits to select one or more wireless signal beams based, at least in part, on measuring one or more received reference signals.
H04B 7/08 - Systèmes de diversité; Systèmes à plusieurs antennes, c. à d. émission ou réception utilisant plusieurs antennes utilisant plusieurs antennes indépendantes espacées à la station de réception
10.
IDENTIFYING OBJECTS USING NEURAL NETWORK-GENERATED DESCRIPTORS
Apparatuses, systems, and techniques are presented to identify one or more objects. In at least one embodiment, one or more neural networks can be used to identify one or more objects based, at least in part, on one or more descriptors of one or more segments of the one or more objects.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 10/26 - Segmentation de formes dans le champ d’image; Découpage ou fusion d’éléments d’image visant à établir la région de motif, p.ex. techniques de regroupement; Détection d’occlusion
G06V 10/77 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source
Apparatuses, systems, and techniques to optimize processor performance. In at least one embodiment, a processor is to perform an application programming interface (API) to exclude one or more portions of program code from a program.
Apparatuses, systems, and techniques to process image frames. In at least one embodiment, an application programming interface (API) is performed to disable frame interpolation to use one or more neural networks.
Apparatuses, systems, and techniques are presented to generate digital content. In at least one embodiment, one or more neural networks are used to generate one or more textured three-dimensional meshes corresponding to one or more objects based, at least in part, one or more two-dimensional images of the one or more objects.
In various examples, the decoding and upscaling capabilities of a client device are analyzed to determine encoding parameters and operations used by a content streaming server to generate encoded video streams. The quality of the upscaled content of the client device may be monitored by the streaming servers such that the encoding parameters may be updated based on the monitored quality. In this way, the encoding operations of one or more streaming servers may be more effectively matched to the decoding and upscaling abilities of one or more client devise such that an increased number of client devices may be served by the streaming servers.
H04N 19/59 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage prédictif mettant en œuvre un sous-échantillonnage spatial ou une interpolation spatiale, p.ex. modification de la taille de l’image ou de la résolution
H04N 19/105 - Sélection de l’unité de référence pour la prédiction dans un mode de codage ou de prédiction choisi, p.ex. choix adaptatif de la position et du nombre de pixels utilisés pour la prédiction
H04N 19/146 - Débit ou quantité de données codées à la sortie du codeur
To improve the efficiency of bounding volumes in a hardware based ray tracer, we employ a sheared axis-aligned bounding box to approximate an oriented bounding box typically defined by rotations. To achieve this, the bounding volume hierarchy builder shears an axis-aligned box to fit tightly around its enclosed oriented geometry in top level or bottom level space, then computes the inverse shear transform. The bounds are still stored as axis-aligned boxes in memory, now defined in the new sheared coordinate system, along with the derived parameters to transform a ray into the sheared coordinate system before testing intersection with the boxes. The ray-bounding volume intersection test is performed as usual, just in the new sheared coordinate system. Additional efficiencies are gained by constraining the number of shear dimensions, constraining the shear transform coefficients to a quantized list, sharing a shear transform across a collection of bounds, performing a shear transform only for ray-bounds testing and not for ray-geometry intersection testing, and adding a specialized shear transform calculator/accelerator to the hardware.
Apparatuses, systems, and techniques to transmit configuration information. In at least one embodiment, a processor includes one or more circuits to wirelessly transmit reference signal configuration information corresponding to one or more reference signals.
Systems, methods, and devices for performing computing operations are provided. In one example, a device is described to include a first processing unit and second processing unit in communication via a network interconnect. The first processing unit is configured to offload at least one of computation tasks and communication tasks to the second processing unit while the first processing unit performs the application-level processing tasks. The second processing unit is also configured to provide a result vector to the first processing unit when the at least one of computation tasks and communication tasks are completed.
Technologies for generating a set of models for each account, where each model is a fine-grained, unsupervised behavior model trained for each user to monitor and detect anomalous patterns are described. An unsupervised training pipeline can generate user models, each being associated with one of multiple accounts and is trained to detect an anomalous pattern using feature data associated with the one account. Each account is associated with at least one of a user, a machine, or a service. An inference pipeline can detect a first anomalous pattern in first data associated with a first account using a first user model. The inference pipeline can detect a second anomalous pattern in second data associated with a second account using a second user model.
In various examples, systems and methods that use dialogue systems associated with various machine systems and applications are described. For instance, the systems and methods may receive text data representing speech, such as a question associated with a vehicle or other machine type. The systems and methods then use a retrieval system(s) to retrieve a question/answer pair(s) associated with the text data and/or contextual information associated with the text data. In some examples, the contextual information is associated with a knowledge base associated with or corresponding to the vehicle. The systems and methods then generate a prompt using the text data, the question/answer pair(s), and/or the contextual information. Additionally, the systems and methods determine, using a language model(s) and based at least on the prompt, an output associated with the text data. For instance, the output may include information that answers the question associated with the vehicle.
Apparatuses, systems, and techniques to use one or more neural networks to generate an upsampled version of one or more images based, at least in part, on a denoised version of said one or more images. At least one embodiment pertains to generating an upsampled high-resolution image from a noisy version and denoised version of a low-resolution image. At least one embodiment pertains to separating components of a low-resolution image before denoising an image.
Techniques applicable to a ray tracing hardware accelerator for traversing a hierarchical acceleration structure with reduced false positive ray intersections are disclosed. The reduction of false positives may be based upon one or more of selectively performing a secondary higher precision intersection test for a bounding volume, identifying and culling bounding volumes that degenerate to a point, and parametrically clipping rays that exceed certain configured distance thresholds.
A circuit for improving control over asynchronous signal crossings during circuit scan tests includes multiple scan registers and a decoder configured to translate a combined output of the scan registers into multiple one-hot controls to the local clock gates of scan registers disposed in multiple different clock domains. Programmable registers are provided to selectively enable and disable the local clock gates of the different clock domains.
Apparatuses, systems, and techniques to generate animations. In at least one embodiment, one or more neural networks control motion of one or more animated objects based, at least in part, on natural language inputs.
G10L 15/06 - Création de gabarits de référence; Entraînement des systèmes de reconnaissance de la parole, p.ex. adaptation aux caractéristiques de la voix du locuteur
G10L 15/16 - Classement ou recherche de la parole utilisant des réseaux neuronaux artificiels
G10L 15/18 - Classement ou recherche de la parole utilisant une modélisation du langage naturel
G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p.ex. dialogue homme-machine
Apparatuses, systems, and techniques to optimize processor performance. In at least one embodiment, a method increases an operation voltage of one or more processors, based at least in part, on one or more error rates of the one or more processors.
Approaches presented herein can provide for the performance of specific types of tasks using a large model, without a need to retrain the model. Custom endpoints can be trained for specific types of tasks, as may be indicated by the specification of one or more guidance mechanisms. A guidance mechanism can be added to or used along with a request to guide the model in performing a type of task with respect to a string of text. An endpoint receiving such a request can perform any marshalling needed to get the request in a format required by the model, and can add the guidance mechanisms to the request by, for example, prepending one or more text strings (or text prefixes) to a text-formatted request. A model receiving this string can process the text according to the guidance mechanisms. Such an approach can allow for a variety of tasks to be performed by a single model.
Apparatuses, systems, and techniques to annotate images using neural models. In at least one embodiment, neural networks generate mask information from labels of one or more objects within one or more images identified by one or more other neural networks.
G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
27.
COMPONENT ANALYSIS FROM MULTIPLE MODALITIES IN AN INTERACTION ENVIRONMENT
Systems and methods integrate different portions of a design review, such as files from a variety of different sources, into an interaction environment for review and interaction by a number of reviewing parties. The reviewing parties interact through an interface that is different from a native software of the files. An automated design review may be performed to evaluate a common rendering, formed from the files, for one or more conflicts, including interferences or version errors.
In various examples, systems and methods are presented for model-based trajectory simulation of agents in a simulated environment. Traffic simulators mimic reality so that autonomous or semi-autonomous vehicle design teams can validate driving models in environments that have diversity and complexity. In some embodiments, for a model-controlled agent of a simulation environment, a plurality of navigation probability distributions are generated, each of the plurality of navigation probability distributions defining a candidate trajectory for the agent to follow. A trajectory is selected for the agent based at least on at least one of the plurality of navigation probability distributions, and the agent is moved within the simulation environment based at least on the selected trajectory. In some embodiments, a search algorithm may be applied across multiple time-steps of a simulation, for example, to identify the occurrence of collision-free sequences of navigation probability distributions.
B60W 60/00 - Systèmes d’aide à la conduite spécialement adaptés aux véhicules routiers autonomes
B60W 30/095 - Prévision du trajet ou de la probabilité de collision
B60W 50/00 - COMMANDE CONJUGUÉE DE PLUSIEURS SOUS-ENSEMBLES D'UN VÉHICULE, DE FONCTION OU DE TYPE DIFFÉRENTS; SYSTÈMES DE COMMANDE SPÉCIALEMENT ADAPTÉS AUX VÉHICULES HYBRIDES; SYSTÈMES D'AIDE À LA CONDUITE DE VÉHICULES ROUTIERS, NON LIÉS À LA COMMANDE D'UN SOUS-ENSEMBLE PARTICULIER - Détails des systèmes d'aide à la conduite des véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier
B60W 50/14 - Moyens d'information du conducteur, pour l'avertir ou provoquer son intervention
Apparatuses, systems, and techniques to perform matrix multiply-accumulate (MMA) operations on data of a first type using one or more MMA instructions for data of a second type. In at least one embodiment, a single tensorfloat-32 (TF32) MMA instruction computes a 32-bit floating point (FP32) output using TF32 input operands converted from FP32 data values.
G06F 7/544 - Méthodes ou dispositions pour effectuer des calculs en utilisant exclusivement une représentation numérique codée, p.ex. en utilisant une représentation binaire, ternaire, décimale utilisant des dispositifs non spécifiés pour l'évaluation de fonctions par calcul
Apparatuses, systems, and techniques are presented to identify and prevent generation of restricted content. In at least one embodiment, one or more neural networks are used to identify restricted content based only on the restricted content.
Approaches presented herein provide for the maintaining of fine details that might be removed by a denoiser used to reduce an amount of noise in an image. An input image can be provided to a denoiser, and can also can be simultaneously processed to extract pixel data that may correspond to fine detail or high frequency features. Individual pixels of an image can have a value determined for a material property sampled for that pixel location, and that value can be compared against an average material property value determined for neighboring pixels. The ratio of material values can be multiplied by the value of a corresponding pixel of the denoised image, for any or all pixel locations, to obtain final pixel values for an output image that include less noise than the original image but represent fine detail that may otherwise have been lost during the denoising process.
G06V 10/60 - Extraction de caractéristiques d’images ou de vidéos relative aux propriétés luminescentes, p.ex. utilisant un modèle de réflectance ou d’éclairage
G06T 3/40 - Changement d'échelle d'une image entière ou d'une partie d'image
Apparatuses, systems, and techniques to perform one or more APIs. In at least one embodiment, a processor is to perform an API to indicate a number of 5G-NR cells that are able to be performed concurrently by one or more processors; a processor is to perform an API to indicate whether one or more processors are able to perform a first number of 5G-NR cells concurrently; a processor comprising one or more circuits is to perform an API to indicate whether one or more resources of one or more processors are allocated to perform 5G-NR cells; and/or a processor comprises one or more circuits to perform an API to indicate one or more techniques to be used by one or more processors in performing one or more 5G-NR cells.
H04W 4/70 - Services pour la communication de machine à machine ou la communication de type machine
G06T 1/20 - Architectures de processeurs; Configuration de processeurs p.ex. configuration en pipeline
H04L 67/133 - Protocoles pour les appels de procédure à distance [RPC]
H04W 4/40 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour les véhicules, p.ex. communication véhicule-piétons
H04W 36/00 - Dispositions pour le transfert ou la resélection
33.
REVERSE EMBEDDED POWER STRUCTURE FOR GRAPHICAL PROCESSING UNIT CHIPS AND SYSTEM-ON-CHIP DEVICE PACKAGES
A die including a die body having a first body surface, a second body surface on an opposite side of the die body as the first body surface, an interconnect region adjacent to the first body surface including interconnect dielectric layers with metal lines and vias, a transistor region above the interconnect region, the metal lines and vias making electrical connections to one or more power rails of the transistor region and electrically connected to transistors of the transistor region, a power region above the transistor region including an electro-conductive film on the second body surface and TSVs in the power region, an outer end of the TSV contacting the film and an embedded end of the TSVs contacting one of the power rails. A method of manufacturing an IC package and computer with the IC package are also disclosed.
H01L 23/528 - Configuration de la structure d'interconnexion
H01L 21/56 - Capsulations, p.ex. couches de capsulation, revêtements
H01L 21/768 - Fixation d'interconnexions servant à conduire le courant entre des composants distincts à l'intérieur du dispositif
H01L 23/00 - DISPOSITIFS À SEMI-CONDUCTEURS NON COUVERTS PAR LA CLASSE - Détails de dispositifs à semi-conducteurs ou d'autres dispositifs à l'état solide
H01L 23/29 - Capsulations, p.ex. couches de capsulation, revêtements caractérisées par le matériau
H01L 23/48 - Dispositions pour conduire le courant électrique vers le ou hors du corps à l'état solide pendant son fonctionnement, p.ex. fils de connexion ou bornes
H01L 23/522 - Dispositions pour conduire le courant électrique à l'intérieur du dispositif pendant son fonctionnement, d'un composant à un autre comprenant des interconnexions externes formées d'une structure multicouche de couches conductrices et isolantes inséparables du corps semi-conducteur sur lequel elles ont été déposées
H01L 25/16 - Ensembles consistant en une pluralité de dispositifs à semi-conducteurs ou d'autres dispositifs à l'état solide les dispositifs étant de types couverts par plusieurs des groupes principaux , ou dans une seule sous-classe de , , p.ex. circuit hybrides
34.
APPLICATION PROGRAMMING INTERFACE TO CAUSE PERFORMANCE OF FRAME INTERPOLATION
Apparatuses, systems, and techniques to process image frames. In at least one embodiment, an application programming interface (API) is performed to cause frame interpolation to be performed using one or more neural networks.
Systems and methods include a first valve that controls a flow rate of a coolant. A processor is configured to set the flow rate of the coolant to a rate that maintains a vapor quality, measured at an outlet of the coolant, within a predetermined quality range.
Apparatuses, systems, and techniques to generate a prompt for one or more machine learning processes. In at least one embodiment, the machine learning process(es) generate(s) a plan to perform a task (identified in the prompt) that is to be performed by an agent (real world or virtual).
Apparatuses, systems, and techniques to perform neural networks. In at least one embodiment, a most consistent output of one or more pre-trained neural networks is to be selected. In at least one embodiment, a most consistent output of one or more pre-trained neural networks is to be selected based, at least in part, on a plurality of variances of one or more inputs to the one or more neural networks.
Systems and techniques are described related to training one or more machine learning models for use in control of a robot. In at least one embodiment, one or more machine learning models are trained based at least on simulations of the robot and renderings of such simulations—which may be performed using one or more ray tracing algorithms, operations, or techniques.
Embodiments of the present disclosure relate to a method of automated tuning of control parameters. In some implementations, the method may include obtaining, from a search algorithm, one or more parameter sets that determine how a controller responds to an environment with at least one changing variable. In these and other implementations, at least one of the parameter sets may include a vector parameter that includes a vector of values. In these and other implementations, a value selected from the vector of values for the vector parameter during operation of the controller may be based on the at least one changing variable. In some implementations, the method may include ordering the vector of values for the vector parameter of the parameter sets and simulating at least one operation of the controller using the parameter sets with the ordered vector of values for the vector parameter.
G05B 13/04 - Systèmes de commande adaptatifs, c. à d. systèmes se réglant eux-mêmes automatiquement pour obtenir un rendement optimal suivant un critère prédéterminé électriques impliquant l'usage de modèles ou de simulateurs
Apparatuses, systems, and techniques adjust a frequency at which a processor operates. In at least one embodiment, a frequency at which a processor operates is adjusted based, at least in part, on different cores of the processor performing one or more identical instructions.
Apparatuses, systems, and techniques to generate a video using two or more images comprising objects to be included in the video. In at least one embodiment, objects are identified in two or more images using one or more neural networks, to generate a video to include the objects in the video.
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p.ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersections; Analyse de connectivité, p.ex. de composantes connectées
G06V 10/74 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques
G06V 10/771 - Sélection de caractéristiques, p.ex. sélection des caractéristiques représentatives à partir d’un espace multidimensionnel de caractéristiques
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
Systems and methods include pressure sensors that measure a pressure differential of coolant between a first coolant line and a second coolant line. Coolant flow control valves control respective valve flow rates. A processor selects a valve from the flow control valves to provide coolant to a coolant output, responsive to the measured pressure differential.
H05K 7/20 - Modifications en vue de faciliter la réfrigération, l'aération ou le chauffage
G01F 1/34 - Mesure du débit volumétrique ou du débit massique d'un fluide ou d'un matériau solide fluent, dans laquelle le fluide passe à travers un compteur par un écoulement continu en utilisant des effets mécaniques en mesurant la pression ou la différence de pression
43.
LANDMARK DETECTION WITH AN ITERATIVE NEURAL NETWORK
Landmark detection refers to the detection of landmarks within an image or a video, and is used in many computer vision tasks such emotion recognition, face identity verification, hand tracking, gesture recognition, and eye gaze tracking. Current landmark detection methods rely on a cascaded computation through cascaded networks or an ensemble of multiple models, which starts with an initial guess of the landmarks and iteratively produces corrected landmarks which match the input more finely. However, the iterations required by current methods typically increase the training memory cost linearly, and do not have an obvious stopping criteria. Moreover, these methods tend to exhibit jitter in landmark detection results for video. The present disclosure improves current landmark detection methods by providing landmark detection using an iterative neural network. Furthermore, when detecting landmarks in video, the present disclosure provides for a reduction in jitter due to reuse of previous hidden states from previous frames.
G06V 20/59 - Contexte ou environnement de l’image à l’intérieur d’un véhicule, p.ex. concernant l’occupation des sièges, l’état du conducteur ou les conditions de l’éclairage intérieur
G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/70 - RECONNAISSANCE OU COMPRÉHENSION D’IMAGES OU DE VIDÉOS Éléments spécifiques à la scène Étiquetage du contenu de scène, p.ex. en tirant des représentations syntaxiques ou sémantiques
G06V 40/16 - Visages humains, p.ex. parties du visage, croquis ou expressions
Systems and techniques for performing multicast-reduction operations. In at least one embodiment, a network device receives first network data associated with a multicast operation to be collectively performed by at least a plurality of endpoints. The network device reserves resources to process second network data to be received from the endpoints, and sends the first network data to a plurality of additional network devices. The network device receives the second network data, and processes the second network data using the reserved resources.
H04L 67/1008 - Sélection du serveur pour la répartition de charge basée sur les paramètres des serveurs, p.ex. la mémoire disponible ou la charge de travail
H04L 67/1012 - Sélection du serveur pour la répartition de charge basée sur la conformité des exigences ou des conditions avec les ressources de serveur disponibles
H04L 67/1014 - Sélection du serveur pour la répartition de charge basée sur le contenu d'une demande
Apparatuses, systems, and techniques to optimize performance of a processor group. In at least one embodiment, a method increases a processor's clock frequency based, at least in part, on performance of other processors in a group.
G06F 1/324 - Gestion de l’alimentation, c. à d. passage en mode d’économie d’énergie amorcé par événements Économie d’énergie caractérisée par l'action entreprise par réduction de la fréquence d’horloge
G06F 1/3237 - Gestion de l’alimentation, c. à d. passage en mode d’économie d’énergie amorcé par événements Économie d’énergie caractérisée par l'action entreprise par désactivation de la génération ou de la distribution du signal d’horloge
G06F 1/3296 - Gestion de l’alimentation, c. à d. passage en mode d’économie d’énergie amorcé par événements Économie d’énergie caractérisée par l'action entreprise par diminution de la tension d’alimentation ou de la tension de fonctionnement
A game-agnostic event detector can be used to automatically identify game events. Game-specific configuration data can be used to specify types of pre-processing to be performed on media for a game session, as well as types of detectors to be used to detect events for the game. Event data for detected events can be written to an event log in a form that is both human- and process-readable. The event data can be used for various purposes, such as to generate highlight videos or provide player performance feedback.
A63F 13/30 - Dispositions d’interconnexion entre des serveurs et des dispositifs de jeu; Dispositions d’interconnexion entre des dispositifs de jeu; Dispositions d’interconnexion entre des serveurs de jeu
A63F 13/426 - Traitement des signaux de commande d’entrée des dispositifs de jeu vidéo, p.ex. les signaux générés par le joueur ou dérivés de l’environnement par mappage des signaux d’entrée en commandes de jeu, p.ex. mappage du déplacement d’un stylet sur un écran tactile en angle de braquage d’un véhicule virtuel incluant des informations de position sur l’écran, p.ex. les coordonnées sur l’écran d’une surface que le joueur vise avec un pistolet optique
A63F 13/428 - Traitement des signaux de commande d’entrée des dispositifs de jeu vidéo, p.ex. les signaux générés par le joueur ou dérivés de l’environnement par mappage des signaux d’entrée en commandes de jeu, p.ex. mappage du déplacement d’un stylet sur un écran tactile en angle de braquage d’un véhicule virtuel incluant des signaux d’entrée de mouvement ou de position, p.ex. des signaux représentant la rotation de la manette d’entrée ou les mouvements des bras du joueur détectés par des accéléromètres ou des gyroscopes
A63F 13/44 - Traitement des signaux de commande d’entrée des dispositifs de jeu vidéo, p.ex. les signaux générés par le joueur ou dérivés de l’environnement incluant la durée ou la synchronisation des opérations, p.ex. l’exécution d’une action dans une certaine fenêtre temporelle
A63F 13/79 - Aspects de sécurité ou de gestion du jeu incluant des données sur les joueurs, p.ex. leurs identités, leurs comptes, leurs préférences ou leurs historiques de jeu
A63F 13/86 - Regarder des jeux joués par d’autres joueurs
G07F 17/32 - Appareils déclenchés par pièces de monnaie pour la location d'articles; Installations ou services déclenchés par pièces de monnaie pour jeux, jouets, sports ou distractions
H04N 21/234 - Traitement de flux vidéo élémentaires, p.ex. raccordement de flux vidéo ou transformation de graphes de scènes MPEG-4
H04N 21/44 - Traitement de flux élémentaires vidéo, p.ex. raccordement d'un clip vidéo récupéré d'un stockage local avec un flux vidéo en entrée ou rendu de scènes selon des graphes de scène MPEG-4
47.
APPLICATION PROGRAMMING INTERFACE TO ACCELERATE MATRIX OPERATIONS
Apparatuses, systems, and techniques to determine a matrix multiplication algorithm for a matrix multiplication operation. In at least one embodiment, a matrix multiplication operation is analyzed to determine an appropriate matrix multiplication algorithm to perform the matrix multiplication algorithm.
In various examples, a time conversion operation may be performed based at least on updating a first local clock of a component based at least on a reference clock of a system including the component. A difference between a current time of the first local clock and a current time of a second local clock of the component may be determined. A state of at least one of the reference clock, the first local clock, or the second local clock may be determined based at least on comparing the time difference to a previously determined difference between a time of the reference clock and a time of the second local clock.
In various examples, techniques for determining perception zones for object detection are described. For instance, a system may use a dynamic model associated with an ego-machine, a dynamic model associated with an object, and one or more possible interactions between the ego-machine and the object to determine a perception zone. The system may then perform one or more processes using the perception zone. For instance, if the system is validating a perception system of the ego-machine, the system may determine whether a detection error associated with the object is a safety-critical error based on whether the object is located within the perception zone. Additionally, if the system is executing within the ego-machine, the system may determine whether the object is a safety-critical object based on whether the object is located within the perception zone.
G05D 1/02 - Commande de la position ou du cap par référence à un système à deux dimensions
G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p.ex. véhicules ou piétons; Reconnaissance des objets de la circulation, p.ex. signalisation routière, feux de signalisation ou routes
50.
USING SCENE-AWARE CONTEXT FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS
In various examples, techniques for using scene-aware context for dialogue systems and applications are described herein. For instance, systems and methods are disclosed that process audio data representing speech in order to determine an intent associated with the speech. Systems and methods are also disclosed that process sensor data representing at least a user in order to determine a point of interest associated with the user. In some examples, the point of interest may include a landmark, a person, and/or any other object within an environment. The systems and methods may then generate a context associated with the point of interest. Additionally, the systems and methods may process the intent and the context using one or more language models. Based on the processing, the language model(s) may output data associated with the speech.
Systems and methods for cooling a datacenter are disclosed. In at least one embodiment, a liquid-to-liquid heat exchanger associated with a rear door of a rack exchanges heat between a primary coolant associated with a chilling facility and a secondary coolant or fluid associated with a computing device of the rack.
An artificial intelligence framework is described that incorporates a number of neural networks and a number of transformers for converting a two-dimensional image into three-dimensional semantic information. Neural networks convert one or more images into a set of image feature maps, depth information associated with the one or more images, and query proposals based on the depth information. A first transformer implements a cross-attention mechanism to process the set of image feature maps in accordance with the query proposals. The output of the first transformer is combined with a mask token to generate initial voxel features of the scene. A second transformer implements a self-attention mechanism to convert the initial voxel features into refined voxel features, which are up-sampled and processed by a lightweight neural network to generate the three-dimensional semantic information, which may be used by, e.g., an autonomous vehicle for various advanced driver assistance system (ADAS) functions.
G06T 17/00 - Modélisation tridimensionnelle [3D] pour infographie
B60W 50/14 - Moyens d'information du conducteur, pour l'avertir ou provoquer son intervention
G06T 3/40 - Changement d'échelle d'une image entière ou d'une partie d'image
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p.ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersections; Analyse de connectivité, p.ex. de composantes connectées
G06V 10/771 - Sélection de caractéristiques, p.ex. sélection des caractéristiques représentatives à partir d’un espace multidimensionnel de caractéristiques
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
State information can be determined for a subject that is robust to different inputs or conditions. For drowsiness, facial landmarks can be determined from captured image data and used to determine a set of blink parameters. These parameters can be used, such as with a temporal network, to estimate a state (e.g., drowsiness) of the subject. To improve robustness, an eye state determination network can determine eye state from the image data, without reliance on intermediate landmarks, that can be used, such as with another temporal network, to estimate the state of the subject. A weighted combination of these values can be used to determine an overall state of the subject. To improve accuracy, individual behavior patterns and context information can be utilized to account for variations in the data due to subject variation or current context rather than changes in state.
G06V 20/59 - Contexte ou environnement de l’image à l’intérieur d’un véhicule, p.ex. concernant l’occupation des sièges, l’état du conducteur ou les conditions de l’éclairage intérieur
B60W 40/08 - Calcul ou estimation des paramètres de fonctionnement pour les systèmes d'aide à la conduite de véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier liés aux conducteurs ou aux passagers
G06F 18/21 - Conception ou mise en place de systèmes ou de techniques; Extraction de caractéristiques dans l'espace des caractéristiques; Séparation aveugle de sources
An alternate root tree or graph structure for ray and path tracing enables dynamic instancing build time decisions to split any number of geometry acceleration structures in a manner that is developer transparent, nearly memory storage neutral, and traversal efficient. The resulting traversals only need to partially traverse the acceleration structure, which improves efficiency. One example use reduces the number of false positive instance acceleration structure to geometry acceleration structure transitions for many spatially separated instances of the same geometry.
In various examples, a corrective operation may be performed based at least in part on detecting that at least one circuit is operating asynchronously with respect to a reference clock. An indication that at least one circuit operating asynchronously was detected may be generated. Upon detecting a circuit operating asynchronously, a corrective operation may be performed such that a component that receives data generated using the at least one circuit continues operating in view of the indication.
In various examples, techniques for detecting occluded objects within an environment are described. For instance, systems and methods may receive training data representing images and ground truth data indicating whether the images are associated with occluded objects or whether the images are not associated with occluded objects. The systems and methods may then train a neural network to detect occluded objects using the training data and the ground truth data. After training, the systems and methods may use the neural network to detect occluded objects within an environment. For instance, while a vehicle is navigating, the vehicle may process sensor data using the neural network. The neural network may then output data indicating whether an object is located within the environment and occluded from view of the vehicle. In some examples, the neural network may further output additional information associated with the occluded object.
G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p.ex. véhicules ou piétons; Reconnaissance des objets de la circulation, p.ex. signalisation routière, feux de signalisation ou routes
G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
G06V 10/776 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source Évaluation des performances
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
57.
IMAGE PROCESSING USING COLOR VISION DEFICIENCY COMPENSATION
The technology disclosed herein involves using a transformation curve to modify colors of images so that those images are more easily viewed by persons with a color vision deficiency (CVD). The transformation curve is applied to spectral versions of images in which each pixel has a spectral representation to modify the spectral versions of the images. A spectral version of an image is modified by, for each pixel of the spectral version of the image, modifying intensities of one or more wavelengths by applying the one or more wavelengths to the transformation curve, which transforms the intensities from source wavelengths to destination wavelengths. The modified spectral version of the image is then modified to a modified version of the image in a color space, such as the RGB color space.
Various techniques for adaptive rendering of images with noise reduction are described. More specifically, the present disclosure relates to approaches for rendering and denoising images—such as ray-traced images—in an iterative process that distributes computational efforts to pixels where denoised output is predicted with higher uncertainty. In some embodiments, an input image may be fed into a deep neural network (DNN) to jointly predict a denoised image and an uncertainty map. The uncertainty map may be used to create a distribution of additional samples (e.g., for one or more samples per pixel on average), and the additional samples may be used with the input image to adaptively render a higher quality image. This process may be repeated in a loop, until some criterion is satisfied, for example, when the denoised image converges to a designated quality, a time or sampling budget is satisfied, or otherwise.
In various examples, metadata may be generated corresponding to compressed data streams that are compressed according to serial compression algorithms—such as arithmetic encoding, entropy encoding, etc.—in order to allow for parallel decompression of the compressed data. As a result, modification to the compressed data stream itself may not be required, and bandwidth and storage requirements of the system may be minimally impacted. In addition, by parallelizing the decompression, the system may benefit from faster decompression times while also reducing or entirely removing the adoption cycle for systems using the metadata for parallel decompression.
A vision transformer (ViT) is a deep learning model that performs one or more vision processing tasks. ViTs may be modified to include a global task that clusters images with the same concept together to produce semantically consistent relational representations, as well as a local task that guides the ViT to discover object-centric semantic correspondence across images. A database of concepts and associated features may be created and used to train the global and local tasks, which may then enable the ViT to perform visual relational reasoning faster, without supervision, and outside of a synthetic domain.
In training a deep neural network using reduced precision, gradient computation operates on larger values without affecting the rest of the training procedure. One technique trains the deep neural network to develop loss, scales the loss, computes gradients at a reduced precision, and reduces the magnitude of the computed gradients to compensate for scaling of the loss. In one example non-limiting arrangement, the training forward pass scales a loss value by some factor S and the weight update reduces the weight gradient contribution by 1/S. Several techniques can be used for selecting scaling factor S and adjusting the weight update.
G06N 3/063 - Réalisation physique, c. à d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
62.
SURFACE PROFILE ESTIMATION AND BUMP DETECTION FOR AUTONOMOUS MACHINE APPLICATIONS
In various examples, surface profile estimation and bump detection may be performed based on a three-dimensional (3D) point cloud. The 3D point cloud may be filtered in view of a portion of an environment including drivable free-space, and within a threshold height to factor out other objects or obstacles other than a driving surface and protuberances thereon. The 3D point cloud may be analyzed—e.g., using a sliding window of bounding shapes along a longitudinal or other heading direction—to determine one-dimensional (1D) signal profiles corresponding to heights along the driving surface. The profile itself may be used by a vehicle—e.g., an autonomous or semi-autonomous vehicle—to help in navigating the environment, and/or the profile may be used to detect bumps, humps, and/or other protuberances along the driving surface, in addition to a location, orientation, and geometry thereof.
In various examples, scenarios may be defined using a declarative description—e.g., defining a behavior of interest—that the present system may convert into a procedural description for generating one or more instances and/or variations of a scenario for testing an autonomous or semi-autonomous machine in a virtual environment. The system may execute observers or evaluators for testing the performance and accuracy of the machine and may compute coverage of various elements based on the generated virtual scenarios, and may feed the results back to the system to generate additional instances and/or variations where the coverage or accuracy is below a desired level. As a result, the system may include an end-to-end framework for generating scenarios in virtual environments, testing and validating the scenarios themselves, and/or testing and validating the underlying autonomous or semi-autonomous systems of the machine—all based on a declarative description.
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p.ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
B60W 50/00 - COMMANDE CONJUGUÉE DE PLUSIEURS SOUS-ENSEMBLES D'UN VÉHICULE, DE FONCTION OU DE TYPE DIFFÉRENTS; SYSTÈMES DE COMMANDE SPÉCIALEMENT ADAPTÉS AUX VÉHICULES HYBRIDES; SYSTÈMES D'AIDE À LA CONDUITE DE VÉHICULES ROUTIERS, NON LIÉS À LA COMMANDE D'UN SOUS-ENSEMBLE PARTICULIER - Détails des systèmes d'aide à la conduite des véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier
G05D 1/00 - Commande de la position, du cap, de l'altitude ou de l'attitude des véhicules terrestres, aquatiques, aériens ou spatiaux, p.ex. pilote automatique
Apparatuses, systems, and techniques to allocate portions of a storage to groups of processors. In at least one embodiment, an amount of storage to store data to be used by one or more computer programs, based at least in part, on an amount of processors to perform one or more portions of the one or more computer programs.
Apparatuses, systems, and techniques to perform software workloads. In at least one embodiment, one or more circuits of a processor cause a programming interface to select a subset of one or more processors of a non-uniform memory access (NUMA) node to perform a software workload.
Apparatuses, systems, and techniques to obtain metric data of a computing resource service provider. In at least one embodiment, metric data of one or more graphics processing unit (GPUs) is caused to be obtained from the one or more GPUs in an order from newest to oldest.
Apparatuses, systems, and techniques to perform software workloads. In at least one embodiment, one or more circuits of a processor cause a first application programming interface to select a second application programming interface, wherein the second application programming interface performs one or more software workloads identified by the first application programming interface.
Apparatuses, systems, and techniques to perform software workloads. In at least one embodiment, one or more circuits of a processor perform a first application programming interface to select a second application programming interface, wherein the second application programming interface monitors performance of one or more software workloads identified by the first application programming interface.
Apparatuses, systems, and techniques to perform software workloads. In at least one embodiment, one or more circuits of a processor perform a first application programming interface to select a second application programming interface, wherein the second application programming interface terminates performance of one or more software workloads identified by the first application programming interface.
Apparatuses, systems, and techniques for scheduling instructions in a cluster to guarantee GPU-CPU alignment for these instructions. In at least one embodiment, jobs are scheduled based on constraints on job sizes and job placement. In at least one embodiment, a processor comprises circuits to schedule instructions to be performed by processors based on latency of interconnects coupled to these processors.
A ray (e.g., a traced path of light, etc.) is generated from an originating pixel within a scene being rendered. Additionally, one or more shadow map lookups are performed for the originating pixel to estimate an intersection of the ray with alpha-tested geometry within the scene. A shadow map stores the distance of geometry as seen from the point of view of the light, and alpha-tested geometry includes objects within the scene being rendered that have a determined texture and opacity. Further, the one or more shadow map lookups are performed to determine a visibility value for the pixel (e.g., that identifies whether the originating pixel is in a shadow) and a distance value for the pixel (e.g., that identifies how far the pixel is from the light). Further still, the visibility value and the distance value for the pixel are passed to a denoiser.
High quality image rendering can be achieved in part by using inverse transform sampling to direct sampling toward regions of greater importance, such as regions with higher brightness values, to reduce noise and improve convergence. Inverse transform sampling can be achieved more efficiently by reformulating as a ray-tracing problem, using tree traversal units that can be accelerated. A geometric mesh can be generated based on a set of cumulative distribution functions (CDFs) for various rows and columns of pixels in a texture, and individual rays can be traced against this mesh, with those rays having a higher probability of intersection at a point with greater importance, such as a higher brightness value. A probability distribution function to be used for importance sampling can be derived by analyzing partial derivatives of the CDF geometry at the intersection location.
A method includes determining, using a processing device, a set of observations from coolant data, the coolant data being received from one or more sensors in an environment associated with a coolant. The method further includes determining, using a machine learning model and the set of observations, a contamination level of the coolant. The method also includes initiating an operation, using the processing device, responsive to determining the coolant contamination level.
G01N 11/02 - Recherche des propriétés d'écoulement des matériaux, p.ex. la viscosité, la plasticité; Analyse des matériaux en déterminant les propriétés d'écoulement en mesurant l'écoulement du matériau
G01N 21/90 - Recherche de la présence de criques, de défauts ou de souillures dans un récipient ou dans son contenu
Systems and methods provide for text normalization or inverse text normalization using a hybrid language system that combines rule-based processing with neural or learned processing. For example, a hybrid rule-based and neural approach identifies semiotic tokens within a textual input and generates a set of potential plain-text conversions of the semiotic tokens. The plain-text conversions are weighted and evaluated by a trained language model that rescores the plain-text conversion based on context to identify a highest scoring plain-text conversion for further processing within a language system pipeline.
G10L 13/08 - Analyse de texte ou génération de paramètres pour la synthèse de la parole à partir de texte, p.ex. conversion graphème-phonème, génération de prosodie ou détermination de l'intonation ou de l'accent tonique
G06F 40/40 - Traitement ou traduction du langage naturel
G10L 13/047 - Architecture des synthétiseurs de parole
75.
CLOUD-BASED UPDATING OF ROOT FILE SYSTEMS USING SYSTEM PARTITIONING
In various examples, systems for performing cloud-based updating of operating systems (e.g., root file systems) using system partitioning. For instance, a system(s) may initiate updates of the operating systems of machines, where the machines use system partitioning for the updating. More specifically, the system(s) may cause a machine to update the operating system using a standby system partition while the machine is currently running on another, active system partition. In some circumstances, the system(s) may perform these processes in order to update a cluster of machines, such as during a specific time period or at a certain frequency. By using such processes, the cluster of machines may still operate during the updating of the machines and/or even if the update fails on one or more of the machines.
G06F 21/57 - Certification ou préservation de plates-formes informatiques fiables, p.ex. démarrages ou arrêts sécurisés, suivis de version, contrôles de logiciel système, mises à jour sécurisées ou évaluation de vulnérabilité
Apparatuses, systems, and techniques to schedule one or more workloads to one or more computers by comparing one or more performance metrics of the one or more workloads to be performed using one or more computers with one or more performance metrics of the one or more workloads to be performed using a simulation of the one or more computers.
Apparatuses, systems, and techniques to select computer systems to perform portions of one or more programs in parallel based, at least in part, on the computer systems' ability to perform the portions at substantially a same performance level. In at least one embodiment, a system includes one or more circuits to select one or more computer systems based, at least in part, on identifying one or more logical partitions of the computer systems based, at least in part, on one or more attributes of one or more programs associated with the one or more computer systems.
Transferring pose to three-dimensional characters is a common computer graphics task that typically involves transferring the pose of a reference avatar to a (stylized) three-dimensional character. Since three-dimensional characters are created by professional artists through imagination and exaggeration, and therefore, unlike human or animal avatars, have distinct shape and features, matching the pose of a three-dimensional character to that of a reference avatar generally requires manually creating shape information for the three-dimensional character that is required for pose transfer. The present disclosure provides for the automated transfer of a reference pose to a three-dimensional character, based specifically on a learned shape code for the three-dimensional character.
Estimating motion of a human or other object in video is a common computer task with applications in robotics, sports, mixed reality, etc. However, motion estimation becomes difficult when the camera capturing the video is moving, because the observed object and camera motions are entangled. The present disclosure provides for joint estimation of the motion of a camera and the motion of articulated objects captured in video by the camera.
One embodiment of a method for controlling a robot includes generating a representation of spatial occupancy within an environment based on a plurality of red, green, blue (RGB) images of the environment, determining one or more actions for the robot based on the representation of spatial occupancy and a goal, and causing the robot to perform at least a portion of a movement based on the one or more actions.
Apparatuses, systems, and techniques for selecting computing resources based on software programs scoring past performance of computing resources. In at least one embodiment, a processor comprising circuitry may cause software programs to be performed using computing resources based on software programs to score past performance of the one or more computing resources. In at least one embodiment, a processor selects a computing system to perform a software workload based on attributes of computer systems that are recorded over periods of time. In at least one embodiment, a processor comprises circuits to use attribute values of computer systems that are computed over periods of time to select computer systems to perform software workloads.
Apparatuses, systems, and techniques to help processing resources used cause one or more systems in a distributed computing environment to be checked by one or more checks. In at least one embodiment, said one or more checks help identify one or more unhealthy nodes based, at least in part, on how many nodes are in a workload.
G06F 11/22 - Détection ou localisation du matériel d'ordinateur défectueux en effectuant des tests pendant les opérations d'attente ou pendant les temps morts, p.ex. essais de mise en route
83.
TECHNIQUES FOR TRANSFERRING COMMANDS TO A DYNAMIC RANDOM-ACCESS MEMORY
Various embodiments include a memory device that is capable of transferring both commands and data via a single clock signal input. In order to initialize the memory device to receive commands, a memory controller transmits a synchronization command to the memory device. The synchronization command establishes command start points that identify the beginning clock cycle of a command that is transferred to the memory device over multiple clock cycles. Thereafter, the memory controller transmits subsequent commands to the memory device according to a predetermined command length. The predetermined command length is based on the number of clock cycles needed to transfer each command to the memory device. Adjacent command start points are separated from one another by the predetermined command length. In this manner, the memory device avoids the need for a second lower speed clock signal for transferring commands to the memory device.
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 - Dispositions pour détecter ou empêcher les erreurs dans l'information reçue
H04L 1/16 - Dispositions pour détecter ou empêcher les erreurs dans l'information reçue en utilisant un canal de retour dans lesquelles le canal de retour transporte des signaux de contrôle, p.ex. répétition de signaux de demande
85.
OBJECT DETECTION USING DEEP LEARNING FOR REAL-TIME STREAMING APPLICATIONS
In various examples, techniques for optimizing object detection models are described herein. Systems and methods are disclosed that process sensor data using a backbone of a machine learning model(s) in order to generate feature maps at different resolutions. The systems and methods then use the machine learning model(s) to generate a vector based at least in part on one or more of the feature maps. For example, if the backbone generates four feature maps, then the machine learning model(s) may generate the vector using two feature maps from the four feature maps. The systems and methods then process the vector using a transformer of the machine learning model(s) in order to generate data representing a class label(s) for an object(s) depicted by an image represented by the sensor data and/or a location(s) of the object(s) within the image.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 10/77 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source
86.
CLUSTERED MICROVIA STRUCTURE FOR A HIGH-DENSITY INTERFACE PCB
According to various embodiments, a printed circuit board includes: a buried via formed through one or more layers of the printed circuit board; a first conductive pad that is formed on a first end of the buried via; a first conductive via that is formed through a first layer of the printed circuit board and is connected to the first conductive pad; and a second conductive via that is formed through the first layer of the printed circuit board and is connected to the first conductive pad.
Apparatuses, systems, and techniques to establish a correspondence between at least a plurality of tensors. In at least one embodiment, information is caused to be stored from one of two or more different tensors having one or more variable dimensions, the one of the two or more different tensors is to represent the two or more different tensors.
G06F 7/57 - Unités arithmétiques et logiques [UAL], c. à d. dispositions ou dispositifs pour accomplir plusieurs des opérations couvertes par les groupes ou pour accomplir des opérations logiques
88.
QUALITY BASED LOAD BALANCING FOR MULTIPATH ROUTING IN NETWORKS
In various examples, one or more network links for transmitting data in a network may be selected based at least in part on a quality associated with the one or more network links. Link quality information associated with a plurality of links may be accessed and analyzed to determine which links to use for transmitting data. Link quality may be evaluated in combination with other link attributes to proactively select network links to minimize network disruption in the event of network equipment failure.
Apparatuses, systems, and techniques for managing memory devices. In at least one embodiment, a processor is provided to assign personalities to one or more memory devices.
Various embodiments include techniques for performing self-synchronizing remote memory operations in a multiprocessor computing system. During a remote memory operation in the multiprocessor computing system, a source processing unit transmits multiple segments of data to a destination processing. For each segment of data, the source processing unit transmits a remote memory operation to the destination processing unit that includes associated metadata that identifies the memory location of a corresponding synchronization object. The remote memory operation along with the metadata is transmitted as a single unit to the destination processing unit. The destination processing unit splits the operation into the remote memory operation and the memory synchronization operation. As a result, the source processing unit avoids the need to perform a separate memory synchronization operation, thereby reducing inter-processor communications and increasing performance of remote memory operations.
In various examples, systems and methods are disclosed that detect hazards on a roadway by identifying discontinuities between pixels on a depth map. For example, two synchronized stereo cameras mounted on an ego-machine may generate images that may be used extract depth or disparity information. Because a hazard's height may cause an occlusion of the driving surface behind the hazard from a perspective of a camera(s), a discontinuity in disparity values may indicate the presence of a hazard. For example, the system may analyze pairs of pixels on the depth map and, when the system determines that a disparity between a pair of pixels satisfies a disparity threshold, the system may identify the pixel nearest the ego-machine as a hazard pixel.
B60W 40/02 - Calcul ou estimation des paramètres de fonctionnement pour les systèmes d'aide à la conduite de véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier liés aux conditions ambiantes
G06T 7/30 - Détermination des paramètres de transformation pour l'alignement des images, c. à d. recalage des images
G06T 7/80 - Analyse des images capturées pour déterminer les paramètres de caméra intrinsèques ou extrinsèques, c. à d. étalonnage de caméra
G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p.ex. véhicules ou piétons; Reconnaissance des objets de la circulation, p.ex. signalisation routière, feux de signalisation ou routes
B60W 60/00 - Systèmes d’aide à la conduite spécialement adaptés aux véhicules routiers autonomes
G06T 7/593 - Récupération de la profondeur ou de la forme à partir de plusieurs images à partir d’images stéréo
92.
RING OSCILLATOR USING MULTI-PHASE SIGNAL REASSEMBLY
Technologies for low jitter and low power ring oscillators with multi-phase signal reassembly are described. A ring oscillator circuit includes a ring oscillator with a set of M delay stages, each stage outputs a phase signal, where M is a positive integer greater than one. The ring oscillator circuit includes a phase selector circuit coupled to the ring oscillator. The phase selector circuit can receive M phase signals from the ring oscillator and generate N phase signals based on the M phase signals, where N is a positive integer less than M.
H03K 19/20 - Circuits logiques, c. à d. ayant au moins deux entrées agissant sur une sortie; Circuits d'inversion caractérisés par la fonction logique, p.ex. circuits ET, OU, NI, NON
H03L 7/099 - Commande automatique de fréquence ou de phase; Synchronisation utilisant un signal de référence qui est appliqué à une boucle verrouillée en fréquence ou en phase - Détails de la boucle verrouillée en phase concernant principalement l'oscillateur commandé de la boucle
In various examples, techniques for using future trajectory predictions for adaptive cruise control (ACC) are described. For instance, a vehicle may determine a future path(s) of the vehicle and a future path(s) of an object(s). The vehicle may then use a speed profile(s) and the future path(s) to determine a trajectory(ies) for the vehicle. The vehicle may then select a trajectory, such as based on the future path(s) of the object(s). Based on the trajectory, ACC of the vehicle may cause the vehicle to navigate at a speed or a velocity. This way, the vehicle is able to continue using ACC even when the driver makes a maneuver(s) or the system determined to make a maneuver, such as switching lanes or choosing a lane when a road splits.
B60W 60/00 - Systèmes d’aide à la conduite spécialement adaptés aux véhicules routiers autonomes
B60W 50/00 - COMMANDE CONJUGUÉE DE PLUSIEURS SOUS-ENSEMBLES D'UN VÉHICULE, DE FONCTION OU DE TYPE DIFFÉRENTS; SYSTÈMES DE COMMANDE SPÉCIALEMENT ADAPTÉS AUX VÉHICULES HYBRIDES; SYSTÈMES D'AIDE À LA CONDUITE DE VÉHICULES ROUTIERS, NON LIÉS À LA COMMANDE D'UN SOUS-ENSEMBLE PARTICULIER - Détails des systèmes d'aide à la conduite des véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier
94.
SECURING CONTENT FROM GUEST VIRTUAL MACHINES FROM UNAUTHORIZED ACCESS BY HOST OPERATING SYSTEMS
In examples, a virtual channel allocated to a virtual machine may be used to transmit image data representing a display surface to memory assigned to the display surface in a virtual address space of the virtual machine. A physical display engine may be configured to fetch the image data from the memory, perform desired transformations, and send the resulting output to the display surface to display. Privileged software may process requests from virtual machines for allocations or configurations of display surfaces and configure the physical display engine, a memory system, and/or other system components accordingly. Software running on a virtual machine may use virtual channels to submit the requests and transmit image data for display surfaces to memory using the virtual address space. A virtual channel may be provided by a virtual network adapter assigned to the virtual machine.
G06F 9/455 - Dispositions pour exécuter des programmes spécifiques Émulation; Interprétation; Simulation de logiciel, p.ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
95.
TEMPORAL INFORMATION PREDICTION IN AUTONOMOUS MACHINE APPLICATIONS
In various examples, a sequential deep neural network (DNN) may be trained using ground truth data generated by correlating (e.g., by cross-sensor fusion) sensor data with image data representative of a sequences of images. In deployment, the sequential DNN may leverage the sensor correlation to compute various predictions using image data alone. The predictions may include velocities, in world space, of objects in fields of view of an ego-vehicle, current and future locations of the objects in image space, and/or a time-to-collision (TTC) between the objects and the ego-vehicle. These predictions may be used as part of a perception system for understanding and reacting to a current physical environment of the ego-vehicle.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p.ex. véhicules ou piétons; Reconnaissance des objets de la circulation, p.ex. signalisation routière, feux de signalisation ou routes
G06F 18/214 - Génération de motifs d'entraînement; Procédés de Bootstrapping, p.ex. ”bagging” ou ”boosting”
G05D 1/00 - Commande de la position, du cap, de l'altitude ou de l'attitude des véhicules terrestres, aquatiques, aériens ou spatiaux, p.ex. pilote automatique
G05D 1/02 - Commande de la position ou du cap par référence à un système à deux dimensions
In various examples, a deep neural network(s) (e.g., a convolutional neural network) may be trained to detect moving and stationary obstacles from RADAR data of a three dimensional (3D) space, in both highway and urban scenarios. RADAR detections may be accumulated, ego-motion-compensated, orthographically projected, and fed into a neural network(s). The neural network(s) may include a common trunk with a feature extractor and several heads that predict different outputs such as a class confidence head that predicts a confidence map and an instance regression head that predicts object instance data for detected objects. The outputs may be decoded, filtered, and/or clustered to form bounding shapes identifying the location, size, and/or orientation of detected object instances. The detected object instances may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.
G01S 7/295 - Moyens pour transformer des coordonnées ou pour évaluer des données, p.ex. en utilisant des calculateurs
G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p.ex. le suivi des coins ou des segments
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
G01S 7/41 - DÉTERMINATION DE LA DIRECTION PAR RADIO; RADIO-NAVIGATION; DÉTERMINATION DE LA DISTANCE OU DE LA VITESSE EN UTILISANT DES ONDES RADIO; LOCALISATION OU DÉTECTION DE LA PRÉSENCE EN UTILISANT LA RÉFLEXION OU LA RERADIATION D'ONDES RADIO; DISPOSITIONS ANALOGUES UTILISANT D'AUTRES ONDES - Détails des systèmes correspondant aux groupes , , de systèmes selon le groupe utilisant l'analyse du signal d'écho pour la caractérisation de la cible; Signature de cible; Surface équivalente de cible
G01S 13/931 - Radar ou systèmes analogues, spécialement adaptés pour des applications spécifiques pour prévenir les collisions de véhicules terrestres
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p.ex. véhicules ou piétons; Reconnaissance des objets de la circulation, p.ex. signalisation routière, feux de signalisation ou routes
Apparatuses, systems, and techniques are described to determine locations of objects using images including digital representations of those objects. In at least one embodiment, a gaze of one or more occupants of a vehicle is determined independently of a location of one or more sensors used to detect those occupants.
G06V 20/59 - Contexte ou environnement de l’image à l’intérieur d’un véhicule, p.ex. concernant l’occupation des sièges, l’état du conducteur ou les conditions de l’éclairage intérieur
G06F 18/21 - Conception ou mise en place de systèmes ou de techniques; Extraction de caractéristiques dans l'espace des caractéristiques; Séparation aveugle de sources
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 40/18 - Caractéristiques de l’œil, p.ex. de l’iris
98.
TRAINING MAINTENANCE SCENARIOS THOUGH ENVIRONMENT SIMULATION
A virtual representation of a physical environment can be generated through simulation, which can include one or more virtual agents to represent robots, or at least semi-automated devices, that can operate and perform various tasks in the physical environment. Various component failures, or other potential problems, can be simulated that can be analyzed by one or more deep learning models associated with the virtual agents. These deep learning models can attempt to diagnose the simulated problem, as well as determine one or more potential solutions. The virtual agents can help to gather information for these determinations, as well as to perform tasks for these potential solutions. Once these deep learning models are trained in this simulated environment, these models can be used by one or more robots to perform tasks that may relate to maintenance or operation of a physical environment.
In various examples, techniques for training and using a task-oriented dialogue system are described. Systems and methods are disclosed for determining, using a prompt model(s) and based at least in part on text data, prompt data representing one or more prompts. Additionally, systems and method are disclosed for determining, using a language model(s) and based at least in part on the text data and the prompt data, a canonical form associated with the text data. In some examples, the prompt model(s) is trained to generate the prompt data that causes the language model(s) to output the canonical form. Systems and method are further disclosed for using the canonical form to determine at least an intent associated with the text data. A dialogue manager may then use the intent to perform one or more actions associated with the text data.
Apparatuses, systems, and techniques to generate reference signals based on configuration information. In at least one embodiment, a processor includes one or more circuits to generate one or more reference signals based, at least in part, on wirelessly transmitted reference signal configuration information.