Apparatuses, systems, and techniques are presented to train neural networks and use those neural networks for inferencing tasks. In at least one embodiment, one or more neural networks are caused to be trained using weight parameters based, at least in part, on an amount of training data used to train the one or more neural networks.
Technologies for enabling downstream components to update upstream states in streaming pipelines are described. One method of a first computing device receives a remote promise object assigned to a first serialized object from a second computing device in the data center over a network fabric. The remote promise object uniquely identifies a first contiguous block of the first serialized object stored in a memory associated with the second computing device. The method obtains contents of the first contiguous block and sends contents of a second serialized object back to the second computing device to release the remote promise object.
A mapping may be made between an array of physical processors and an array of functional logical processors. Also, a mapping may be made between logical memory channels (associated with the logical processors) and functional physical memory channels (associated with the physical processors). These mappings may be stored within one or more tables, which may then be used to bypass faulty processors and memory channels when implementing memory accesses, while optimizing locality (e.g., by minimizing the proximity of memory channels to processors).
G06F 15/80 - Architectures of general purpose stored program computers comprising an array of processing units with common control, e.g. single instruction multiple data processors
G06F 12/06 - Addressing a physical block of locations, e.g. base addressing, module addressing, address space extension, memory dedication
4.
REMOTE DESCRIPTOR TO ENABLE REMOTE DIRECT MEMORY ACCESS (RDMA) TRANSPORT OF A SERIALIZED OBJECT
Technologies for enabling remote direct memory access (RDMA) transport of serialized objects in streaming pipelines are described. One method of a first computing device that stores a serialized object in a first memory can generate a remote descriptor associated with the serialized object. The remote descriptor uniquely identifies the location of the serialized object and a reference count token. The first computing device sends the remote descriptor to a second computing device in the data center over a network fabric. The second computing device uses the remote descriptor to obtain the contiguous block from the first memory for storage at a second memory associated with the second computing device. The value of the reference count token can be updated by receiving a message from the second computing device, and the remote descriptor can be released responsive to the value of the reference count token satisfying a threshold value.
G06F 15/173 - Interprocessor communication using an interconnection network, e.g. matrix, shuffle, pyramid, star or snowflake
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
5.
FREQUENCY-LOCKED AND PHASE-LOCKED LOOP-BASED CLOCK GLITCH DETECTION FOR SECURITY
A glitch detection device includes an oscillator to generate multiple local clocks of multiple different phases and a sampling circuit to oversample, using the multiple local clocks, a system clock to generate multiple samples of the system clock. The device further includes digital logic that in turn includes a glitch detector to monitor a variation in pulse width of the system clock based on counting the multiple samples and to report a glitch in response to detecting a variation in the pulse width that exceeds a threshold value. The digital logic further includes a loop filter coupled between the glitch detector and the oscillator. The loop filter variably adjusts the oscillator based on a frequency of each of the multiple samples to control an output frequency of each of the multiple different phases of the oscillator.
H03L 7/099 - Automatic control of frequency or phase; Synchronisation using a reference signal applied to a frequency- or phase-locked loop - Details of the phase-locked loop concerning mainly the controlled oscillator of the loop
H03L 7/089 - Automatic control of frequency or phase; Synchronisation using a reference signal applied to a frequency- or phase-locked loop - Details of the phase-locked loop concerning mainly the frequency- or phase-detection arrangement including the filtering or amplification of its output signal the phase or frequency detector generating up-down pulses
6.
ADAPTIVE TASK SCHEDULING FOR VIRTUALIZED ENVIRONMENTS
Apparatuses, systems, and techniques to use a graphics processing unit are disclosed. In at least one embodiment, a length of a timeslice for executing work on a virtual machine is determined based, at least in part, on the amount of time prior work performed by a GPU exceeded a prior length of a timeslice. In some embodiments, a work portion and a preemption portion of a timeslice are changed or updated based on an amount of time spent performing prior work and an amount of time spent performing preemption of the prior work.
Apparatuses, systems, and techniques for buffer identification of an application for post-processing. The apparatuses, systems, and techniques includes generating a buffer statistic data structure for a buffer of a plurality of buffers associated with a frame of an application; updating the buffer statistic data structure with metadata of the draw call responsive to detecting a draw call to the buffer; and determining, based on the buffer statistic data structure, a score reflecting a likelihood of the buffer being associated with a specified buffer type.
Approaches for training data set size estimation for machine learning model systems and applications are described. Examples include a machine learning model training system that estimates target data requirements for training a machine learning model, given an approximate relationship between training data set size and model performance using one or more validation score estimation functions. To derive a validation score estimation function, a regression data set is generated from training data, and subsets of the regression data set are used to train the machine learning model. A validation score is computed for the subsets and used to compute regression function parameters to curve fit the selected regression function to the training data set. The validation score estimation function is then solved for and provides an output of an estimate of the number additional training samples needed for the validation score estimation function to meet or exceed a target validation score.
Systems and methods for datacenter are disclosed. In at least one embodiment, a system or method herein causes a process for a structure that includes a component tracking system, where such a system includes individual components within individual servers or individual racks, the process being based in part on at least location information and configuration information from one or more of an optical sensor or a radio sensor associated with a motile-support that is adapted for at least three dimensional (3D) movement in a space having the individual servers or the individual racks.
Approaches presented herein provide for the generation of synthesized data from input noise using a denoising diffusion network. A higher order differential equation solver can be used for the denoising process, with one or more higher-order terms being distilled into one or more separate efficient neural networks. A separate, efficient neural network can be called together with a primary denoising model at inference time without significant loss in sampling efficiency. The separate neural network can provide information about the curvature (or other higher-order term) of the differential equation, representing a denoising trajectory, that can be used by the primary diffusion network to denoise the image using fewer denoising iterations.
In various examples, a timer component that generates an event when an interrupt request has not yet been cleared within at least a predetermined amount of time.
Apparatuses, systems, and techniques to optimize processor performance. In at least one embodiment, a method optimizes linked code based, at least in part, on storing an indication of whether two portions of code have been linked.
A VLSI placement optimization framework receives a cell connectivity representation and cell characteristics and uses self-supervised graph clustering to optimize cell cluster assignments for power, performance, and area (PPA). The framework provides cell clustering constraints as placement guidance to commercial placers. Specifically, graph learning techniques are used to formulate the PPA metrics as machine learning loss functions that can be minimized directly through gradient descent. The framework improves the PPA metrics at the placement stage and the improvements endure to the post-route stage.
Apparatuses, systems, and techniques to evaluate neural networks. In at least one embodiment, neural networks are evaluated using one or more other neural networks. In at least one embodiment, two or more neural networks are caused to generate consistent results from first input information and caused to generate inconsistent results from second input information.
In various examples, estimating optimal training data set sizes for machine learning model systems and applications. Systems and methods are disclosed that estimate an amount of data to include in a training data set, where the training data set is then used to train one or more machine learning models to reach a target validation performance. To estimate the amount of training data, subsets of an initial training data set may be used to train the machine learning model(s) in order to determine estimates for the minimum amount of training data needed to train the machine learning model(s) to reach the target validation performance. The estimates may then be used to generate one or more functions, such as a cumulative density function and/or a probability density function, wherein the function(s) is then used to estimate the amount of training data needed to train the machine learning model(s).
A method for processing includes receiving a definition of a processing pipeline including multiple sequential processing stages. The processing pipeline is partitioned into a plurality of partitions. The first partition of the processing pipeline is executed on a first computational accelerator, whereby the first computational accelerator writes output data from a final stage of the first partition to an output buffer in a first memory. The output data are copied over a packet communication network to an input buffer in a second memory. The second partition of the processing pipeline is executed on a second computational accelerator using the copied output data in the second memory as input data to a first stage of the second partition.
Apparatuses, systems, and techniques for caching of compiled shader programs in a cloud computing environment. An initial request for a compiled shader program for an application executed by a first client device is received. The initial request includes a first shader key generated based on first state data. If the compiled shader program is determined, based on the first shader key, to not be stored using a shader cache, the compiled shader program is received from the first client device and stored with the first shader key using the shader cache. A subsequent request for the compiled shader program is received for the application running at a second client device. The subsequent request includes a second shader key generated based on second state data. If the second shader key is determined to match the first shader key, the compiled shader program is transmitted to the second client device.
Neural network-based structures for action user equipment device detection, estimation of time-of-arrival, and estimation of carrier frequency offset utilized with the narrowband physical random-access channel of wireless communication systems. The structure includes a neural network to generate predictions of active user equipment devices, and a twin neural network to generate time-of-arrival predictions for signals from the user equipment devices and carrier frequency offset predictions for signals from the user equipment devices.
In various examples, systems and methods are disclosed relating to multi-domain generative adversarial networks with learned warp fields. Input data can be generated according to a noise function and provided as input to a generative machine-learning model. The generative machine-learning model can determine a plurality of output images each corresponding to one of a respective plurality of image domains. The generative machine-learning model can include at least one layer to generate a plurality of morph maps each corresponding to one of the respective plurality of image domains. The output images can be presented using a display device.
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 10/77 - Arrangements 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
In order to perform denoising on a three-dimensional (3D) spherical measurement of light (such as spherical irradiance probe information or the results of a 3D gonioreflectometry capture), the 3D spherical measurement of light is converted to a two-dimensional (2D) measurement by creating multiple copies of the 3D spherical measurement of light, determining a two-dimensional sub-domain (e.g., a rectangular sub-domain) for each of the multiple copies, and stitching the plurality of two-dimensional sub-domains together in a toroidal configuration. Denoising may then be performed on this 2D measurement via a machine learning implementation or other means. This may result in more accurate 3D spherical light probes that require fewer light measurement samples to generate accurate light measurements.
Systems and methods for cooling a datacenter are disclosed. In at least one embodiment, system includes one or more processors to use one or more neural networks to control a direction of air flow caused by one or more fans to cool a coolant circulating within a liquid-to-air heat exchanger or to cool one or more servers of a rack, based at least in part on sensor data associated with at least one of: the coolant or the rack.
Voltage level conversion circuits include PMOS pull-down devices or NMOS pull-up devices, and inverters with outputs that determine gate voltages of these devices. The inverters are powered by moving supply voltages, for example complementary supply voltages generated from a pair of cross-coupled inverters. The cross-coupled inverters may implement a data storage latch with the moving supply voltages generated from the internal data storage nodes of the latch.
Systems and methods for cooling a datacenter are disclosed. In at least one embodiment, a primary cooling loop includes at least one primary flow controller to control flow of a primary coolant to a coolant distribution unit (CDU) at a primary flow rate that is determined based in part on heat generated from one or more computing devices that is to be addressed by a secondary coolant, which is to be cooled in a CDU by a primary coolant at a primary flow rate enabled by at least one primary flow controller.
An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data—and ultimately a fused high definition (HD) map—that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.
G01C 21/00 - Navigation; Navigational instruments not provided for in groups
G01C 21/16 - Navigation; Navigational instruments not provided for in groups by using measurement of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
In various examples, a three-dimensional (3D) data processing pipeline for autonomous systems and applications is presented. Systems and methods are disclosed for 3D point cloud data processing fused with video analysis applications. Using the systems and methods described herein, processing of 3D data may be performed in different multimedia frameworks, allowing a user to use common libraries and/or to implement custom libraries on top of the existing system design. As a result, conventional 2D video processing may be combined with 3D data processing, to allow for data representing a flat 2D world to represent a rich 3D world. In this way, the fused 3D depth and/or range data with 2D camera image data allows for perception and/or vision that is more powerful, accurate, and precise.
In various embodiments, an unsupervised training application executes a neural network on a first point cloud to generate keys and values. The unsupervised training application generates output vectors based on a first query set, the keys, and the values and then computes spatial features based on the output vectors. The unsupervised training application computes quantized context features based on the output vectors and a first set of codes representing a first set of 3D geometry blocks. The unsupervised training application modifies the first neural network based on a likelihood of reconstructing the first point cloud, the quantized context features, and the spatial features to generate an updated neural network. A trained machine learning model includes the updated neural network, a second query set, and a second set of codes representing a second set of 3D geometry blocks and maps a point cloud to a representation of 3D geometry instances.
In various examples, a cell model that partitions a geographic region into one or more cells is used to determine clusters of cell which share similarities. Sensor data is provided to one or more machine learning models trained to classify the sensor data to one or more cells of the cell model. Based on classifying sensor data to cells of a cell model, similarities between pairings of cells of the cell model may be determined and used to form clusters of the cell which are sufficiently similar in order to aid in the curation of training data used to train machine learning models in order to aid an autonomous or semi-autonomous machine in a surrounding environment.
G06F 16/387 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using geographical or spatial information, e.g. location
G06N 3/04 - Architecture, e.g. interconnection topology
Apparatuses, systems, and techniques to parse textual data using parallel computing devices. In at least one embodiment, text is parsed by a plurality of parallel processing units using a finite state machine and logical stack to convert the text to a tree data structure. Data is extracted from the tree by the plurality of parallel processors and stored.
In various embodiments, an unsupervised training application trains a machine learning model to generate representations of point clouds. The unsupervised training application executes a neural network on a first point cloud representing a first three-dimensional (3D) scene to generate segmentations. Based on the segmentations, the unsupervised training application computes spatial features. The unsupervised training application computes quantized context features based on the segmentations and a first set of codes representing a first set of 3D geometry blocks. The unsupervised training application modifies the neural network based on a likelihood of reconstructing the first point cloud, the quantized context features, and the spatial features to generate an updated neural network. A trained machine learning model that includes the updated neural network and a second set of codes representing a second set of 3D geometry blocks maps a point cloud representing a 3D scene to a representation of 3D geometry instances.
Apparatuses, systems, and techniques for pre-loading a software application in a cloud computing environment. A method can include sending a pre-load request to pre-load a first portion of data for an application hosted at an application hosting platform, the pre-load request being received before receiving user input identifying the application for execution. The method can include receiving a first indication that the first portion of data is pre-loaded and receiving a user request to execute the application. The method can further include sending a load request to load a second portion of data for the application, receiving a second indication that the second portion of data is loaded for the application, and causing the application to execute at the virtualized computing environment in response to receiving the second indication.
Apparatuses, systems, and techniques to evaluate neural networks. In at least one embodiment, neural networks are evaluated using one or more other neural networks. In at least one embodiment, two or more neural networks are caused to generate consistent results from first input information and caused to generate inconsistent results from second input information.
A neural network is trained to identify one or more features of an image. The neural network is trained using a small number of original images, from which a plurality of additional images are derived. The additional images generated by rotating and decoding embeddings of the image in a latent space generated by an autoencoder. The images generated by the rotation and decoding exhibit changes to a feature that is in proportion to the amount of rotation.
G06V 10/772 - Determining representative reference patterns, e.g. averaging or distorting patterns; Generating dictionaries
G06F 7/57 - Arithmetic logic units [ALU], i.e. arrangements or devices for performing two or more of the operations covered by groups or for performing logical operations
G06F 17/18 - Complex mathematical operations for evaluating statistical data
G06N 3/088 - Non-supervised learning, e.g. competitive learning
Techniques are disclosed herein for generating a content item. The techniques include receiving a content item and metadata indicating a level of corruption associated with the content item; and for each iteration included in a plurality of iterations: performing one or more operations to add corruption to a first version of the content item to generate a second version of the content item, and performing one or more operations to reduce corruption in the second version of the content item to generate a third version of the content item, wherein a level of corruption associated with the third version of the content item is less than a level of corruption associated with the first version of the content item.
Techniques are disclosed herein for generating a content item. The techniques include receiving a content item and metadata indicating a level of corruption associated with the content item; and for each iteration included in a plurality of iterations: performing one or more operations to add corruption to a first version of the content item to generate a second version of the content item, and performing one or more operations to reduce corruption in the second version of the content item to generate a third version of the content item, wherein a level of corruption associated with the third version of the content item is less than a level of corruption associated with the first version of the content item.
Volumetric quantification can be performed for various parameters of an object represented in volumetric data. Multiple views of the object can be generated, and those views provided to a set of neural networks that can generate inferences in parallel. The inferences from the different networks can be used to generate pseudo-labels for the data, for comparison purposes, which enables a co-training loss to be determined for the unlabeled data. The co-training loss can then be used to update the relevant network parameters for the overall data analysis network. If supervised data is also available then the network parameters can further be updated using the supervised loss.
Various embodiments include a system for managing cache memory in a computing system. The system includes a sectored cache memory that provides a mechanism for sharing sectors in a cache line among multiple cache line allocations. Traditionally, different cache line allocations are assigned to different cache lines in the cache memory. Further, cache line allocations may not use all of the sectors of the cache line, leading to low utilization of the cache memory. With the present techniques, multiple cache lines share the same cache line, leading to improved cache memory utilization relative to prior techniques. Further, sectors of cache allocations can be assigned to reduce data bank conflicts when accessing cache memory. Reducing such data bank conflicts can result in improved memory access performance, even when cache lines are shared with multiple allocations.
A system, such as for use in an automobile, is configured to process image data that includes infrared values and visible light values (e.g., data generated by a red, green, blue, infrared (RGB-IR) sensor). The system determines how to blend IR data and visible light data together to generate optimal images according to current light levels. In embodiments, the system computes a scene detection value for the image data based on a comparison between the infrared values and the visible light values. The system can then determine an amount of infrared correction, a color correction factor, a color saturation factor, etc. to apply to the image data. The system then transforms the image data based on the amount of infrared correction, the color correction factor, the color saturation factor, etc. The transformed image data includes more information for low light scenes than is traditionally available, and thus produces higher quality images in embodiments.
A circuit system includes an integrated circuit package mounted on a first side of a printed circuit board and a power regulator connected to power terminals of the integrated circuit package through a cutout in the printed circuit board. The power regulator draws power from the printed circuit board by way of side pins around a periphery of the cutout.
Apparatuses, systems, and techniques to indicate capabilities of a neural network. In at least one embodiment, a processor includes one or more circuits to indicate one or more capabilities of a neural network.
In embodiments a graphics pipeline includes a logic that can assess whether to enable or disable tiled rendering for sets of graphics primitives. The logic applies one or more rules or heuristics to a set of graphics primitives associated with a frame, and determines whether to enable tiled rendering for that set of graphics primitives if the one or more rules or heuristics are satisfied. Otherwise, the logic determines to disable tiled rendering for that set of graphics primitives. As further graphics primitives are received for the frame, the logic may make additional decisions as to whether or not to render the further graphics primitives using tiled rendering.
In various examples, shader bindings may be recorded in a shader binding table that includes shader records. Geometry of a 3D scene may be instantiated using object instances, and each may be associated with a respective set of the shader records using a location identifier of the set of shader records in memory. The set of shader records may represent shader bindings for an object instance under various predefined conditions. One or more of these predefined conditions may be implicit in the way the shader records are arranged in memory (e.g., indexed by ray type, by sub-geometry, etc.). For example, a section selector value (e.g., a section index) may be computed to locate and select a shader record based at least in part on a result of a ray tracing query (e.g., what sub-geometry was hit, what ray type was traced, etc.).
An apparatus includes at least one heat pipe that is adapted to be thermally coupled to an integrated circuit and has an evaporator portion and a first condenser portion, wherein the first condenser portion extends away from the evaporator portion; a first plurality of cooling fins that is attached to the first condenser portion; a first movable support that is thermally coupled to the first condenser portion and is configured to move a second plurality of cooling fins relative to the first plurality of cooling fins; and the second plurality of cooling fins, which is attached to the first movable support.
In various examples, techniques for multi-dimensional tracking of objects using two-dimensional (2D) sensor data are described. Systems and methods may use first image data to determine a first 2D detected location and a first three-dimensional (3D) detected location of an object. The systems and methods may then determine a 2D estimated location using the first 2D detected location and a 3D estimated location using the first 3D detected location. The systems and methods may use second image data to determine a second 2D detected location and a second 3D detected location of a detected object, and may then determine that the object corresponds to the detected object using the 2D estimated location, the 3D estimated location, the second 2D detected location, and the second 3D detected location. The systems and method then generate, modify, delete, or otherwise update an object track that includes 2D state information and 3D state information.
Devices, systems, and techniques to incorporate lighting effects into computer-generated graphics. In at least one embodiment, a virtual scene comprising a plurality of lights is rendered by randomly sampling a set of lights from among the plurality of lights prior to rendering a frame of graphics. A subset of the set of lights is selected and used to render pixels within one or more portions of the frame.
Neural network performance is improved in terms of training speed, memory footprint, and/or accuracy by learning a compressed neural graphics primitive representation. A neural graphics primitive is a mathematical function involving at least one neural network, used to represent a computer graphic, where the graphic can be an image, a 3D shape, a light field, a signed distance function, a radiance field, 2D video, volumetric video, etc. Instead of being input directly to a neural network, inputs are effectively mapped (encoded) into a higher dimensional space via a function. The input comprises coordinates used to identify a point within a d-dimensional space. The point is quantized and a set of vertex coordinates corresponding to the point are used to access an indexing codebook and a features codebook that store learned index offsets and learned feature vectors, respectively. The learned feature vectors are then provided as inputs to the neural network.
An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data—and ultimately a fused high definition (HD) map—that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.
C03C 17/36 - Surface treatment of glass, e.g. of devitrified glass, not in the form of fibres or filaments, by coating with at least two coatings having different compositions at least one coating being a metal
48.
GUIDING VEHICLES THROUGH VEHICLE MANEUVERS USING MACHINE LEARNING MODELS
In various examples, a trigger signal may be received that is indicative of a vehicle maneuver to be performed by a vehicle. A recommended vehicle trajectory for the vehicle maneuver may be determined in response to the trigger signal being received. To determine the recommended vehicle trajectory, sensor data may be received that represents a field of view of at least one sensor of the vehicle. A value of a control input and the sensor data may then be applied to a machine learning model(s) and the machine learning model(s) may compute output data that includes vehicle control data that represents the recommended vehicle trajectory for the vehicle through at least a portion of the vehicle maneuver. The vehicle control data may then be sent to a control component of the vehicle to cause the vehicle to be controlled according to the vehicle control data.
B60W 30/00 - Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
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
49.
RAY TRACING USING RESERVOIR RESAMPLING WITH SPATIAL SHIFT-MAPPING
Disclosed are apparatuses, systems, and techniques to render images with global illumination using efficient ray tracing, light source identification, and reservoir resampling that deploys temporal and spatial reservoirs.
A circuit system includes an integrated circuit package mounted on a first side of a printed circuit board and a power regulator connected to power terminals of the integrated circuit package through a cutout in the printed circuit board. The power regulator draws power from the printed circuit board by way of connections on a shelf region extending beyond an area of the cutout.
H05K 1/18 - Printed circuits structurally associated with non-printed electric components
H05K 3/34 - Assembling printed circuits with electric components, e.g. with resistor electrically connecting electric components or wires to printed circuits by soldering
H01L 23/36 - Selection of materials, or shaping, to facilitate cooling or heating, e.g. heat sinks
H01L 23/50 - Arrangements for conducting electric current to or from the solid state body in operation, e.g. leads or terminal arrangements for integrated circuit devices
51.
WAVELENGTH-DIVISION MULTIPLEXED LINKS WITH BUILT-IN CLOCK FORWARDING
A system can include an optical transmitter having transmitter components and an optical receiver having receiver components and photodetectors. The optical transmitter is configured to receive optical wavelengths of radiation from a multiple wavelength generate, such as a laser, and generate transmitted wavelengths including data wavelengths and excess wavelengths. Each photodetector is configured to receive at least one transmitted wavelength. The photodetectors can include a common photodetector operatively coupled to at least two receiver components and configured to obtain a set of unmodulated carrier frequencies (e.g., a pair of unmodulated carrier frequencies) from the at least two receiver components, and determine clock information therefrom. The clock information can be determined by obtaining a heterodyne frequency from the set of unmodulated carrier frequencies. The heterodyne frequency can be used to synchronize the optical transmitter and the optical receiver.
In various examples, techniques for multi-dimensional tracking of objects using two-dimensional (2D) sensor data are described. Systems and methods may use first image data to determine a first 2D detected location and a first three-dimensional (3D) detected location of an object. The systems and methods may then determine a 2D estimated location using the first 2D detected location and a 3D estimated location using the first 3D detected location. The systems and methods may use second image data to determine a second 2D detected location and a second 3D detected location of a detected object, and may then determine that the object corresponds to the detected object using the 2D estimated location, the 3D estimated location, the second 2D detected location, and the second 3D detected location. The systems and method then generate, modify, delete, or otherwise update an object track that includes 2D state information and 3D state information.
In various examples, systems and methods for tracking objects and determining time-to-collision values associated with the objects are described. For instance, the systems and methods may use feature points associated with an object depicted in a first image and feature points associated with a second image to determine a scalar change associated with the object. The systems and methods may then use the scalar change to determine a translation associated with the object. Using the scalar change and the translation, the systems and methods may determine that the object is also depicted in the second image. The systems and methods may further use the scalar change and a temporal baseline to determine a time-to-collision associated with the object. After performing the determinations, the systems and methods may output data representing at least an identifier for the object, a location of the object, and/or the time-to-collision.
Approaches for an advanced AI-assisted vehicle can utilize an extensive suite of sensors inside and outside the vehicle, providing information to a computing platform running one or more neural networks. The neural networks can perform functions such as facial recognition, eye tracking, gesture recognition, head position, and gaze tracking to monitor the condition and safety of the driver and passengers. The system also identifies and tracks body pose and signals of people inside and outside the vehicle to understand their intent and actions. The system can track driver gaze to identify objects the driver might not see, such as cross-traffic and approaching cyclists. The system can provide notification of potential hazards, advice, and warnings. The system can also take corrective action, which may include controlling one or more vehicle subsystems, or when necessary, autonomously controlling the entire vehicle. The system can work with vehicle systems for enhanced analytics and recommendations.
B60W 50/00 - CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - Details of control systems for road vehicle drive control not related to the control of a particular sub-unit
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
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
G05D 1/00 - Control of position, course, altitude, or attitude of land, water, air, or space vehicles, e.g. automatic pilot
One embodiment of the present invention sets forth an integrated circuit. The integrated circuit includes a plurality of subunits associated with a plurality of operating voltages. The integrated circuit also includes one or more voltage regulator circuits that convert a first input voltage into a first plurality of output voltages during a first test, wherein the plurality of output voltages is delivered to the plurality of subunits via a plurality of output channels.
09 - Scientific and electric apparatus and instruments
Goods & Services
Computer hardware; computer peripherals; computer networking hardware; electronic adapters; computer networking adapters; embedded processors; integrated circuits, semiconductors and chips; host channel adapters; target channel adapters; computer networking switches; routers; backplanes; host bus adapters; downloadable software drivers; downloadable and recorded computer networking software; downloadable and recorded software for transmitting data among servers, computers and data storage devices; graphics processing units (GPUs); computer hardware for enabling connections among central processing units (CPUs), servers and data storage devices; computer hardware and downloadable and recorded software for enhancing the performance of artificial intelligence and machine learning software applications; peripheral component interconnect express (PCIe) expansion cards; network interface cards; network management interface hardware; computer buses; computer hardware that enables peripheral component interconnect express (PCIe) access to multiple central processing units (CPUs); computer hardware that enables bus access to multiple central processing units (CPUs)
57.
DISTRIBUTED GLOBAL AND LOCAL REFERENCE VOLTAGE GENERATION
A method includes generating a differential voltage from a first reference voltage generator; receiving the differential voltage at a second reference voltage generator; dividing the differential voltage at the second reference voltage generator into multiple available reference voltage levels; and selecting one of the available reference voltage levels to apply to a circuit.
G11C 11/4074 - Power supply or voltage generation circuits, e.g. bias voltage generators, substrate voltage generators, back-up power, power control circuits
58.
LOOK AHEAD SWITCHING CIRCUIT FOR A MULTI-RANK SYSTEM
A multi-rank circuit system utilizing a shared IO channel includes a first stage of multiple selectors coupled to input multiple digital busses, and a second stage including one or more selectors coupled to receive outputs of the first stage of selectors and to individually select one of the outputs of the first stage of selectors to one or more control circuits for IO circuits of the ranks. The system switches one of the ranks to be an active rank on the shared IO channel, and operates the first stage of selectors to select one of the digital busses to the second stage of selectors in advance of switching a next active rank to the shared IO channel.
G11C 11/4093 - Input/output [I/O] data interface arrangements, e.g. data buffers
H03K 17/687 - Electronic switching or gating, i.e. not by contact-making and -breaking characterised by the use of specified components by the use, as active elements, of semiconductor devices the devices being field-effect transistors
59.
TRAINING AND CONFIGURATION OF REFERENCE VOLTAGE GENERATORS IN A MULTI-RANK CIRCUIT SYSTEM
The differential voltage output from a first reference voltage generator of a multi-rank circuit is trained on multiple ranks of the multi-rank circuit. Multiple local reference voltage generators are trained to generate reference voltages for communication on the individual ranks, where the reference voltages output by the local reference voltage generators fall within a range of the differential voltage output.
G11C 11/4074 - Power supply or voltage generation circuits, e.g. bias voltage generators, substrate voltage generators, back-up power, power control circuits
60.
USING INTRINSIC FUNCTIONS FOR SHADOW DENOISING IN RAY TRACING APPLICATIONS
In examples, threads of a schedulable unit (e.g., a warp or wavefront) of a parallel processor may be used to sample visibility of pixels with respect to one or more light sources. The threads may receive the results of the sampling performed by other threads in the schedulable unit to compute a value that indicates whether a region corresponds to a penumbra (e.g., using a wave intrinsic function). Each thread may correspond to a respective pixel and the region may correspond to the pixels of the schedulable unit. A frame may be divided into the regions with each region corresponding to a respective schedulable unit. In denoising ray-traced shadow information, the values for the regions may be used to avoid applying a denoising filter to pixels of regions that are outside of a penumbra while applying the denoising filter to pixels of regions that are within a penumbra.
In various examples, sensor data—such as masked sensor data—may be used as input to a machine learning model to determine a confidence for object to person associations. The masked sensor data may focus the machine learning model on particular regions of the image that correspond to persons, objects, or some combination thereof. In some embodiments, coordinates corresponding to persons, objects, or combinations thereof, in addition to area ratios between various regions of the image corresponding to the persons, objects, or combinations thereof, may be used to further aid the machine learning model in focusing on important regions of the image for determining the object to person associations.
G06T 7/246 - Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
G06V 10/26 - Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
G06V 20/52 - Surveillance or monitoring of activities, e.g. for recognising suspicious objects
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
Manufacturers perform tests on chips before the chips are shipped to customers. However, defects can occur on a chip after the manufacturer testing and when the chips are used in a system or device. The defects can occur due to aging or the environment in which the chip is employed and can be critical; especially when the chips are used in systems such as autonomous vehicles. To verify the structural integrity of the IC during the lifetime of the product, an in-system test (IST) is disclosed. The IST enables self-testing mechanisms for an IC in working systems. The IST mechanisms provide structural testing of the ICs when in a functional system and at a manufacturer's level of testing. Unlike ATE tests that are running on a separate environment, the IST provides the ability to go from a functional world view to a test mode.
G01R 31/3177 - Testing of logic operation, e.g. by logic analysers
G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
G06F 11/36 - Preventing errors by testing or debugging of software
A machine learning model (MLM) may be trained and evaluated. Attribute-based performance metrics may be analyzed to identify attributes for which the MLM is performing below a threshold when each are present in a sample. A generative neural network (GNN) may be used to generate samples including compositions of the attributes, and the samples may be used to augment the data used to train the MLM. This may be repeated until one or more criteria are satisfied. In various examples, a temporal sequence of data items, such as frames of a video, may be generated which may form samples of the data set. Sets of attribute values may be determined based on one or more temporal scenarios to be represented in the data set, and one or more GNNs may be used to generate the sequence to depict information corresponding to the attribute values.
A multi-rank circuit system includes multiple transmitters each switchably coupled to a first end of a shared input/output (IO) channel and a unified receiver coupled to a second end of the shared IO channel. The unified receiver is coupled to apply a preconfigured analog reference voltage to set a differential output of the unified receiver, and further configured to apply a variable digital code to adjust the differential output according to a particular one of the transmitters that is switched to the shared IO channel.
A multi-rank system includes multiple circuit ranks communicating over a common data line to multiple data receivers, each corresponding to one or more of the ranks and each having a corresponding reference voltage generator and clock timing adjustment circuit, such that a rank to communicate on the shared data line is switched without reconfiguring outputs of either the reference voltage generators or the clock timing adjustment circuits.
In various examples, system and methods for stereo disparity based hazard detection for autonomous machine applications are presented. Example embodiments may assist an ego-machine in detecting hazards within its path of travel. The systems and methods may use disparity between a stereo pair of images to generate a baseline path disparity model and further identify hazards from detected disparities that deviate from that path disparity model. A disparity map for the image pair is constructed in which each pixel represents a disparity for a corresponding element of the image captured. Blockwise division may be optionally used to subdivide the disparity map into a plurality of smaller disparity maps, each corresponding to a block of pixels of the disparity map. A V-space disparity map, where a first axis corresponds to disparity values and the second axis corresponds to pixel rows, may be used to simplify estimation of the path disparity model.
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
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
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 10/75 - Image or video pattern matching; Proximity measures in feature spaces using context analysis; Selection of dictionaries
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
67.
DETECTING HAZARDS BASED ON DISPARITY MAPS USING MACHINE LEARNING FOR AUTONOMOUS MACHINE SYSTEMS AND APPLICATIONS
In various examples, systems and methods for machine learning based hazard detection for autonomous machine applications using stereo disparity are presented. Disparity between a stereo pair of images is used to generate a path disparity model. Using the path disparity model, a machine learning model can recognize when a pixel in the first image corresponds to a pixel in the second image even though the pixel in the two images does not have identical characteristics. Similarities in extracted feature vectors can be computed and represented by a vector similarity metric that is input to a machine learning classifier, along with feature information extracted from the stereo image pair, to differentiate hazard pixels from non-hazard pixels. In some embodiments, a V-space disparity map, where a first axis corresponds to disparity values and the second axis corresponds to pixel rows, may be used to simplify estimation of the path disparity model.
G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
G06T 7/55 - Depth or shape recovery from multiple images
G06V 10/74 - Image or video pattern matching; Proximity 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
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
68.
DETECTING HARDWARE FAULTS IN DATA PROCESSING PIPELINES
In various examples, a system comprising at least one circuit to detect whether a fault has occurred during performance of an operation by the at least one circuit. In at least some embodiments, the at least one circuit generates error detecting values and determines a fault has occurred when the error detecting values do not match predetermined error detecting data.
A system comprises at least one circuit to detect whether a fault has occurred during performance of an operation by the at least one circuit. The at least one circuit generates error detecting values and determines a fault has occurred when the error detecting values do not match predetermined error detecting data.
Apparatuses, systems, and techniques to collaboratively train one or more machine learning models. Parameter reviewers may be configured to compare sets of machine learning model parameter information in order to generate one or more machine learning models, such as neural networks.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Data processing units; computer chips; semiconductor chips; multiprocessor chips; computer switches; ethernet switches; computer data center switches; computer networking hardware; computer networking peripherals; communication and computer network hubs; cards and microprocessors for computers; graphics processing units (GPUs); integrated circuits; integrated circuits, semiconductors and computer chipsets; system on chip processors; embedded processors; servers; accelerator cards; network accelerators for artificial intelligence computing; downloadable and recorded network software; downloadable and recorded software for use in relation to data processing units and computer networking hardware for facilitating data transmission, data processing, data collection, data analysis, and decision-making; downloadable and recorded software and computer hardware for digital signal processing, managing, configuring, customizing, updating, monitoring and connecting radio access networks; computer hardware and downloadable and recorded software and firmware featuring artificial intelligence for use in the fields of edge computing, high performance computing, distributed computing, machine learning, automation controls, data collection, data analytics and internet of things; downloadable and recorded software, firmware, and computer hardware for edge computing, high performance computing, distributed computing, machine learning, automation controls, data collection, data analytics, and the internet of things; computer hardware and downloadable and recorded software for virtual private cloud (VPC) networking; computer hardware and downloadable and recorded software for remote direct memory access over converged Ethernet (RoCE) networking; data processing units that consist of high-performance, software-programmable, multi-core central processing units (CPUs), network interfaces and programmable acceleration engines to accelerate and offload artificial intelligence networking; data processing units for processing, analyzing, storing, transferring, organizing and managing artificial intelligence compute data in data centers; data processing units for enhancing the performance of artificial intelligence and machine learning applications; data processing units for compute and network virtualization; data processing units for use in the fields of artificial intelligence, machine learning, high performance computing, scientific computing, data center operations, computer networking, cloud-native supercomputing; data processing units that enable the secure transmission of compute data between servers for artificial intelligence workloads Design and development of computer hardware for computer networking and data centers; design and development of data processing units, graphics processing units, edge computing systems, platforms, gateways, and devices; design and development of radio access networks; providing fog and edge computing services featuring online non-downloadable software for use in generating, collecting, and analyzing data, and the operation of computer, storage and networking services; platform as a service (PaaS) services featuring computer software platforms to enable access to a decentralized supercomputer consisting of a pool of devices connected to the internet; providing edge computing servers; providing online non-downloadable software for virtual private cloud (VPC) networking; providing online non-downloadable software for remote direct memory access over converged Ethernet (RoCE) networking
72.
IDENTIFYING IDLE PROCESSORS USING NON-INTRUSIVE TECHNIQUES
Apparatuses, systems, and techniques to classify computing devices as idle or busy using a machine learning (ML) model based on power consumption data collected non-intrusively are described. One method receives power consumption data for a computing device from a service processor operatively coupled to the computing device. The method determines a set of features from the power consumption data for a first time period. The method classifies, using an ML model and the set of features, whether the computing device is idle or busy in the first time period. The method outputs an indication of the computing device being idle responsive to a classification that the computing device is idle.
Systems and methods provide a pipeline to develop and deploy machine learning models by using query/response pairs from a different machine learning model as training data. A set of model parameters are established and a trained machine learning models provides responses to input queries to develop query/response pairs. These query/response pairs may be used to train a different machine learning model. That model can be tested against the original model to determine whether they are in agreement, and when the models are in agreement the different machine learning model can be deployed as the primary model for the system.
An integrated circuit including a chip substrate having an upper isolation layer with a pad thereon and a coil located below the pad, where, in a dimension perpendicular to a surface of the chip substrate, a perimeter of the coil overlaps with a perimeter of the pad.
H01L 23/522 - Arrangements for conducting electric current within the device in operation from one component to another including external interconnections consisting of a multilayer structure of conductive and insulating layers inseparably formed on the semiconductor body
H01L 23/00 - SEMICONDUCTOR DEVICES NOT COVERED BY CLASS - Details of semiconductor or other solid state devices
H01L 27/02 - Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate including integrated passive circuit elements with at least one potential-jump barrier or surface barrier
75.
PRINTED CIRCUIT BOARD ASSEMBLY WITH INTEGRATED VAPOR CHAMBER
A printed circuit board assembly comprises: a printed circuit board (PCB); an integrated circuit (IC) package that is mounted on the PCB and includes a first surface and a bare IC die disposed on the first surface; and a vapor chamber coupled to the first surface of the IC package.
In various examples, at least partial control of a vehicle may be transferred to a control system remote from the vehicle. Sensor data may be received from a sensor(s) of the vehicle and the sensor data may be encoded to generate encoded sensor data. The encoded sensor data may be transmitted to the control system for display on a virtual reality headset of the control system. Control data may be received by the vehicle and from the control system that may be representative of a control input(s) from the control system, and actuation by an actuation component(s) of the vehicle may be caused based on the control input.
One embodiment of a method for rendering one or more graphics images includes sampling one or more directions of light passing through a medium, computing one or more parameters associated with one or more position distributions based on the one or more directions of light, computing a brightness in a direction at which the light exits the medium based on the one or more parameters associated with the one or more position distributions and the direction at which the light exits the medium, and rendering the one or more graphics images based on the brightness in the direction at which the light exits the medium.
Approaches presented herein provide for reduction in bandwidth and other resources used for lighting determinations in a rendering process. Sample locations for traced rays in a data volume can be determined by sampling a probability function based on random numbers and density values of macrocells through which those ray pass. Data for the macrocells may be stored and processed using different processors, and there may be no sample locations selected for a given macrocell, such as where the macrocell has a very low maximum density value. If it is determined that no sample locations are contained within a given macrocell through which a ray passes, the ray data is not forwarded to a processor for that macrocell but can instead be forwarded to the processor (if different) for a next macrocell that contains a sample location. Such an approach conserves resources and improves system efficiency by reducing the number of communications and processing operations to be performed.
Various types of image analysis benefit from a multi-stream architecture that allows the analysis to consider shape data. A shape stream can process image data in parallel with a primary stream, where data from layers of a network in the primary stream is provided as input to a network of the shape stream. The shape data can be fused with the primary analysis data to produce more accurate output, such as to produce accurate boundary information when the shape data is used with semantic segmentation data produced by the primary stream. A gate structure can be used to connect the intermediate layers of the primary and shape streams, using higher level activations to gate lower level activations in the shape stream. Such a gate structure can help focus the shape stream on the relevant information and reduces any additional weight of the shape stream.
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 10/80 - Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
In various examples, a gaze direction of a user's eyes may be tracked and synced with perception data of the vehicle to determine POIs that the user is interested in. In some examples, POIs may be stored as waypoints in a waypoint catalog or store and included as part of a map. As a user is driving in a vehicle down a roadway, a system onboard the vehicle may access the map to determine locations of the vehicle, and may reference the waypoint catalog to determine the POIs that the vehicle passes. Using an advertiser name, contact information, an advertisement image, advertiser website information, links to additional content, etc. relating to each waypoint, a log of the passed POIs may be stored for access by the user.
In various examples, a lane planner for generating lane planner output data based on a state and probabilistic action space is provided. A driving system—that operates based on a hierarchical drive planning framework—includes the lane planner and other planning and control components. The lane planner processes lane planner input data (e.g., large lane graph, source node, target node) to generate lane planner output data (e.g., expected time rewards). The driving system can also include a route planner (e.g., a first planning layer) that operates to provide the lane planner input data to the lane planner. The lane planner operates as second planning layer that processes the lane planner input data based at least in part on a state and probabilistic action space of the large lane graph and calculates a time cost associated with navigating from a source node to a target node in the large lane graph.
In at least one embodiment, a solid state storage device (“SSD”) service provider provides an application programming interface (“API”) that allows an application to specify a first personality type for the requested SSD. In at least one embodiment, the SSD service provider provides an API of the requested type, but may fulfil the request using an SSD having a second SSD personality type by translating calls from the first SSD personality type to the second SSD personality type.
Devices, systems, and techniques for provisioning of cloud-based machine learning training, optimization, and deployment services. The techniques include providing, to a remote client device, a list of available machine learning models (MLMs), receiving from the remote client device an indication of selected MLM(s) from the provided list, identifying training settings for selected MLM(s), identifying a training data for the selected MLM(s), configuring, using the identified training settings, execution of one or more processes to train the selected MLM(s) using the identified training data, and providing to the remote client device a representation of completed training of at least one MLM.
Devices, systems, and techniques for experiment-based training of machine learning models (MLMs) using early stopping. The techniques include starting training tracks (TTs) that train candidate MLMs using the same training data and respective sets of training settings, performing a first evaluation of a first candidate MLM prior to completion of a corresponding first TT, and responsive to the first evaluation, placing the first TT on an inactive status, inactive status indicating that further training of the first candidate MLM is to be ceased. The techniques further include continuing at least a second TT using the training data, and responsive to conclusion of the TTs, selecting, as one or more final MLMs, the first candidate MLM or a second candidate MLM.
Digital low-dropout micro voltage regulator configured to accept an external voltage and produce a regulated voltage. All active devices of the voltage regulator are digital devices. All signals of the voltage regulator, except the first voltage and the regulated voltage, may be characterized as digital signals. Some active devices of the voltage regulator may be physically separated from other active devices of the voltage regulator by active devices of non-voltage regulator circuitry.
12 - Land, air and water vehicles; parts of land vehicles
38 - Telecommunications services
Goods & Services
Manufacturing machines, namely, machinery used for manufacturing of integrated circuits and semiconductors; Machines for the manufacture of semiconductors; Robots; Industrial robots; IC handlers, namely, machines for processing integrated circuits; Robotic arms for industrial purposes; 3D printers; structural parts and fittings for all of the aforesaid Vehicles, namely, land vehicles, water vehicles being boats and amphibious vehicles, air vehicles being airplanes, aircraft, helicopters and drones, railway vehicles being railway cars and trains, and space vehicles; Cars; Land vehicles; Automobiles; Autonomous cars; Autonomous vehicles, namely, unmanned vehicles and self-driving vehicles; Self-driving transport vehicles; Robotic cars; land vehicle structural parts; Structural parts for self-driving cars; Vehicle anti-theft and security equipment, namely, anti-theft and security devices for vehicles; Land vehicles featuring autonomous driving features and structural parts and accessories therefor; Driverless cars; Driverless transporter vehicles, namely, self-driving transport vehicles; Driverless cars, namely, autonomous cars; Electric vehicles, namely, electric cars, trucks, boats, amphibious vehicles, railway cars, trains and space vehicles for use on land, water, rails, or space; Electric vehicles, namely, electric land vehicles, electric automobiles, electric cars; autonomous cars based on artificial intelligence solutions; Vehicle interior trim kits comprising dashboard trim panels, instrument cluster surround, radio/climate control bezel; Robotic and self-driving transport vehicles that transport people, packages, and freight; Driverless land vehicles; Connected vehicles, namely, land vehicles connected to internet; Remote controlled land vehicles; vehicles for locomotion by land; structural electrical assemblies adapted for automotive and electric vehicles, namely, instrument cluster assemblies, modular cockpit assemblies, and transmission speed sensors; Components, instruments and accessories for land vehicles, namely, instrument clusters and electrical controllers; Rear-seat entertainment (RSE) systems specially adapted for automobiles; Dashboards for vehicles; Vehicle instrument panel, namely, front dash panel Telecommunications consultation; Internet broadcasting services; Video broadcasting; Audio broadcasting; Video conferencing services; Broadcasting of audio and video content and programming over the internet; Streaming user generated photographic and video content via a website on the internet and via mobile electronic devices; Providing internet chat rooms; Providing on-line forums for transmission of messages among computer users; Transfer of data by telecommunication; Video on demand transmissions; Communications services, namely, transmitting streamed sound and audiovisual recordings via the Internet by means of computer terminals, video game consoles or hand-held games with liquid crystal displays; Broadcasting services and provision of telecommunication access to video and audio content provided via a video-on demand service via the Internet; Wireless electronic transmission of voice signals, data, facsimiles, images and information; Consultancy in the field of audio, text and visual data transmission and communication for telecommunication other than broadcasting; Communication by computer terminals; Providing communication transmission over value added telecommunication networks, namely, information transmission via electronic communications networks; Rental of telecommunication equipment for accessing to communication networks; Providing technical support services in the nature of telecommunications consultation services regarding the usage of communications equipment; Providing facilities and equipment for video conferencing; Audio and video teleconferencing services; Electronic exchange of voice, data, audio, video, text and graphics via the internet and telecommunications networks; Providing electronic bulletin boards for transmission of messages among users in the field of general interest; Providing an online community forum for users to share and stream information, audio, video, real-time news, entertainment content, or information; Photo sharing and video sharing services, namely, electronic transmission of digital photo files, videos and audio visual content among internet users; Providing access to computer, electronic and online databases; Telecommunications services, namely, electronic transmission of data, messages, graphics, images, audio, video and information; Providing online forums for communication on topics of general interest; Providing online communications links which transfer mobile device and internet users to other local and global online locations; Facilitating access to third party websites or to other electronic third party content via a universal login; Providing online chat rooms, email and instant messaging services, and electronic bulletin boards; Audio, text and video broadcasting services over the internet or other communication networks; Voice over internet protocol (VOIP) services; Telephony communication services; Peer-to-peer photo and data sharing services, namely, electronic transmission of digital photo and video files, graphics and audio content among internet users; Telecommunications and peer-to-peer network computer services, namely, electronic transmission of images, audio-visual and video content, photographs, videos, data, text, messages, advertisements, media advertising communications and information; Providing user access to digital content, namely, text, audio, video, images, still and motion pictures, graphics, computer games, video games, signals, messages and multimedia files; Telecommunications services, namely, electronic transmission of virtual and augmented reality content and data; information, consultancy and advisory services relating to all of the aforesaid
12 - Land, air and water vehicles; parts of land vehicles
38 - Telecommunications services
Goods & Services
Manufacturing machines, namely, machinery used for manufacturing of integrated circuits and semiconductors; Machines for the manufacture of semiconductors; Robots; Industrial robots; IC handlers, namely, machines for processing integrated circuits; Robotic arms for industrial purposes; 3D printers; structural parts and fittings for all of the aforesaid Vehicles, namely, land vehicles, water vehicles being boats and amphibious vehicles, air vehicles being airplanes, aircraft, helicopters and drones, railway vehicles being railway cars and trains, and space vehicles; Cars; Land vehicles; Automobiles; Autonomous cars; Autonomous vehicles, namely, unmanned vehicles and self-driving vehicles; Self-driving transport vehicles; Robotic cars; land vehicle structural parts; Structural parts for self-driving cars; Vehicle anti-theft and security equipment, namely, anti-theft and security devices for vehicles; Land vehicles featuring autonomous driving features and structural parts and accessories therefor; Driverless cars; Driverless transporter vehicles, namely, self-driving transport vehicles; Driverless cars, namely, autonomous cars; Electric vehicles, namely, electric cars, trucks, boats, amphibious vehicles, railway cars, trains and space vehicles for use on land, water, rails, or space; Electric vehicles, namely, electric land vehicles, electric automobiles, electric cars; autonomous cars based on artificial intelligence solutions; Vehicle interior trim kits comprising dashboard trim panels, instrument cluster surround, radio/climate control bezel; Robotic and self-driving transport vehicles that transport people, packages, and freight; Driverless land vehicles; Connected vehicles, namely, land vehicles connected to internet; Remote controlled land vehicles; vehicles for locomotion by land; structural electrical assemblies adapted for automotive and electric vehicles, namely, instrument cluster assemblies, modular cockpit assemblies, and transmission speed sensors; Components, instruments and accessories for land vehicles, namely, instrument clusters and electrical controllers; Rear-seat entertainment (RSE) systems specially adapted for automobiles; Dashboards for vehicles; Vehicle instrument panel, namely, front dash panel Telecommunications consultation; Internet broadcasting services; Video broadcasting; Audio broadcasting; Video conferencing services; Broadcasting of audio and video content and programming over the internet; Streaming user generated photographic and video content via a website on the internet and via mobile electronic devices; Providing internet chat rooms; Providing on-line forums for transmission of messages among computer users; Transfer of data by telecommunication; Video on demand transmissions; Communications services, namely, transmitting streamed sound and audiovisual recordings via the Internet by means of computer terminals, video game consoles or hand-held games with liquid crystal displays; Broadcasting services and provision of telecommunication access to video and audio content provided via a video-on demand service via the Internet; Wireless electronic transmission of voice signals, data, facsimiles, images and information; Consultancy in the field of audio, text and visual data transmission and communication for telecommunication other than broadcasting; Communication by computer terminals; Providing communication transmission over value added telecommunication networks, namely, information transmission via electronic communications networks; Rental of telecommunication equipment for accessing to communication networks; Providing technical support services in the nature of telecommunications consultation services regarding the usage of communications equipment; Providing facilities and equipment for video conferencing; Audio and video teleconferencing services; Electronic exchange of voice, data, audio, video, text and graphics via the internet and telecommunications networks; Providing electronic bulletin boards for transmission of messages among users in the field of general interest; Providing an online community forum for users to share and stream information, audio, video, real-time news, entertainment content, or information; Photo sharing and video sharing services, namely, electronic transmission of digital photo files, videos and audio visual content among internet users; Providing access to computer, electronic and online databases; Telecommunications services, namely, electronic transmission of data, messages, graphics, images, audio, video and information; Providing online forums for communication on topics of general interest; Providing online communications links which transfer mobile device and internet users to other local and global online locations; Facilitating access to third party websites or to other electronic third party content via a universal login; Providing online chat rooms, email and instant messaging services, and electronic bulletin boards; Audio, text and video broadcasting services over the internet or other communication networks; Voice over internet protocol (VOIP) services; Telephony communication services; Peer-to-peer photo and data sharing services, namely, electronic transmission of digital photo and video files, graphics and audio content among internet users; Telecommunications and peer-to-peer network computer services, namely, electronic transmission of images, audio-visual and video content, photographs, videos, data, text, messages, advertisements, media advertising communications and information; Providing user access to digital content, namely, text, audio, video, images, still and motion pictures, graphics, computer games, video games, signals, messages and multimedia files; Telecommunications services, namely, electronic transmission of virtual and augmented reality content and data; information, consultancy and advisory services relating to all of the aforesaid
88.
VIDEO STREAMING SCALING USING VIRTUAL RESOLUTION ADJUSTMENT
In various examples, network conditions associated with a video stream are observed over time to determine a virtual scaling factor that may be applied to the images of one or more frames of the video stream to generate scaled images that may be appended with a padding region that maintains the original resolution of the video and/or video stream such that a receiving device may crop the padding region from the received video stream during the decoding process without restarting or including additional intra-coded frames.
H04N 21/2343 - Processing of video elementary streams, e.g. splicing of video streams or manipulating MPEG-4 scene graphs involving reformatting operations of video signals for distribution or compliance with end-user requests or end-user device requirements
H04N 21/4402 - Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to MPEG-4 scene graphs involving reformatting operations of video signals for household redistribution, storage or real-time display
89.
DISTANCE ESTIMATION TO OBJECTS AND FREE-SPACE BOUNDARIES IN AUTONOMOUS MACHINE APPLICATIONS
In various examples, a deep neural network (DNN) is trained—using image data alone—to accurately predict distances to objects, obstacles, and/or a detected free-space boundary. The DNN may be trained with ground truth data that is generated using sensor data representative of motion of an ego-vehicle and/or sensor data from any number of depth predicting sensors—such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. The DNN may be trained using two or more loss functions each corresponding to a particular portion of the environment that depth is predicted for, such that—in deployment—more accurate depth estimates for objects, obstacles, and/or the detected free-space boundary are computed by the DNN.
B60W 60/00 - Drive control systems specially adapted for autonomous road vehicles
G06V 20/56 - Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
G06F 18/214 - Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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
90.
REAL-TIME VIDEO CONFERENCE CHAT FILTERING USING MACHINE LEARNING MODELS
In various examples, as a user is speaking or presenting content during an online video conference, the data stream may be processed to generate a textual representation (e.g., transcript) of the audio and/or information relating to the video. The textual representation and/or video related information may then be processed to determine a context or one or more topic(s) of discussion. Based on the determined context/topic(s), a corresponding neural network(s) may be selected. Once a neural network has been selected, comments may be retrieved from a chat feature of the application and applied to the neural network. The neural network may then output data to indicate the relevance of the comments to the determined discussion topic. Based on the relevance of the comment, the comment may be allowed, prioritized, deleted, de-emphasized, or otherwise filtered in the chat feature.
Self-supervised machine learning is applied to combinational gate sizing based on an input circuit netlist. A transformer neural network architecture is disclosed to select gate sizes along paths of the network between primary inputs/outputs and/or sequential logic elements. The gate size selections may be optimized along dimensions such as path delay, path power consumption, and path circuit area.
Various embodiments include techniques for performing speculative remote memory operation tracking in a multiprocessor computing system. Conventionally, transfers of data between processors and other components of a computing system require memory synchronization operations to determine that the data is valid and coherent before the data is transferred from a destination to a requesting source. Existing techniques for performing these memory synchronization operations are increasingly inefficient as the number of components in a computing system increases, particularly for remote memory operations. The disclosed techniques track remote memory operations and speculatively perform these memory synchronization operations. As a result, a given memory synchronization operation is often complete prior to the corresponding remote memory operation arrives at the destination, leading to improved efficiency and performance of remote memory operations in complex computing systems.
Apparatuses, systems, and techniques for reliable image data capture are disclosed herein. A system includes a sensor configured to receive light reflected off one or more objects in an environment. The sensor includes a first set of sensor pixels configured to detect a portion of the received light having wavelengths in the visible light spectrum. The sensor further includes a second set of sensor pixels configured to detect an additional portion of the received light having wavelengths in an infrared spectrum. The system further includes a filter component configured to reduce an intensity of the portion of the received light detected by the first set of sensor pixels while maintaining at least a minimum intensity of the additional portion of the received light detected by the second set of sensor pixels.
H04N 23/73 - Circuitry for compensating brightness variation in the scene by influencing the exposure time
G06V 10/143 - Sensing or illuminating at different wavelengths
H04N 23/11 - Cameras or camera modules comprising electronic image sensors; Control thereof for generating image signals from different wavelengths for generating image signals from visible and infrared light wavelengths
H04N 23/74 - Circuitry for compensating brightness variation in the scene by influencing the scene brightness using illuminating means
In various examples, systems and methods are disclosed relating to aliasing control of program variables in storage via automatic application of artificial dependences during program compilation. In some implementations, a system can include a detector to automatically detect a pattern, based at least on a structure of data flow in a source program, indicative of sequences of dependent operations, where the sequences are independent from one another. The system can determine a storage aliasing preference for whether to allow the compiler to allocate the program variables of the respective sequences to the same processor storage locations, or to prevent the compiler from doing so. The system can assign one or more annotations to the source program indicative of one or more artificial dependences for a compiler to respect when performing program transformations prior to the allocation of program variables.
In various examples, a method to manage map data includes storing a map of a geographic area using an immutable tree. The immutable tree comprises a plurality of nodes stored using a distributed hash table. The plurality of nodes include a plurality of map tiles. At least two map tiles of the plurality of map tiles cover different geographic subregions of the geographic area of the map. The method includes hosting one or more binary large objects (BLOBs) that correspond to the plurality of map tiles in an origin data plane. The method includes making the one or more BLOBs available for distribution to one or more client devices using a content delivery network (CDN).
One embodiment of a computer-implemented method for processing ray tracing operations in parallel includes receiving a plurality of rays and a corresponding set of importance sampling instructions for each ray included in the plurality of rays for processing, wherein each ray represents a path from a light source to at least one point within a three-dimensional (3D) environment, and each corresponding set of importance sampling instruction is based at least in part on one or more material properties associated with at least one surface of at least one object included in the 3D environment; assigning each ray included in the plurality of rays to a different processing core included in a plurality of processing cores; and for each ray included in the plurality of rays, causing the processing core assigned to the ray to execute the corresponding set of importance sampling instructions on the ray to generate a direction for a secondary ray that is produced when the ray intersects a surface of an object within the 3D environment.
G06F 15/80 - Architectures of general purpose stored program computers comprising an array of processing units with common control, e.g. single instruction multiple data processors
G06F 9/48 - Program initiating; Program switching, e.g. by interrupt
In various examples, systems and methods are disclosed that preserve rich spatial information from an input resolution of a machine learning model to regress on lines in an input image. The machine learning model may be trained to predict, in deployment, distances for each pixel of the input image at an input resolution to a line pixel determined to correspond to a line in the input image. The machine learning model may further be trained to predict angles and label classes of the line. An embedding algorithm may be used to train the machine learning model to predict clusters of line pixels that each correspond to a respective line in the input image. In deployment, the predictions of the machine learning model may be used as an aid for understanding the surrounding environment—e.g., for updating a world model—in a variety of autonomous machine applications.
G06F 18/2411 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
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 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
G06V 10/48 - Extraction of image or video features by mapping characteristic values of the pattern into a parameter space, e.g. Hough transformation
G06V 10/94 - Hardware or software architectures specially adapted for image or video understanding
G06V 10/75 - Image or video pattern matching; Proximity measures in feature spaces using context analysis; Selection of dictionaries
98.
3D ENVIRONMENT RECONSTRUCTION FOR PERSISTENT OBJECT TRACKING
In various examples, a 3D representation of an environment may be generated from sensor data, with objects being detected in the environment using the sensor data and stored as items that can be tracked and located within the 3D representation. The 3D representation of the environment and item information may be used to determine (e.g., identify or predict) a location or position of an item within the 3D representation and/or recommend a storage location for the item within the 3D representation. Using a determined location or position, one or more routes to the location through the 3D representation may be determined. Data corresponding to a determined route may be provided to a user and/or device. User preferences, permissions, roles, feedback, historical item data, and/or other data associated with a user may be used to further enhance various aspects of the disclosure.
Systems and methods for cooling a datacenter are disclosed. In at least one embodiment, a server tray or box includes a surface with a flow-through fixture or manifold that extends on both sides of the surface, that includes an inward coupling, an outward coupling, a flow controller, and a state sensor, the state sensor to monitor a flow-through fixture or manifold, where a flow controller of a flow-through fixture or manifold can change a flow of a coolant through a flow-through fixture or manifold and can selectively trap a portion of a coolant within a flow-through fixture or manifold.
A circuit board includes chip die mounted on a three dimensional rectangular structure, a three dimensional triangular prism structure, or a combination thereof. A ball grid array for the chip die mounted on any such three dimensional structure is interposed between the three dimensional structure and the circuit board itself.
