Abstract: Apparatuses, systems, and techniques to detect whether fifth-generation new radio (5G-NR) devices are transmitting data. In at least one embodiment, a receiver detects whether 5G-NR devices are transmitting 5G-NR signals based on a number of 5G-NR devices sharing a same time and frequency.

Abstract: Apparatuses, systems, and techniques to schedule one or more workloads to one or more computers by comparing one or more performance metrics of the one or more workloads to be performed using one or more computers with one or more performance metrics of the one or more workloads to be performed using a simulation of the one or more computers.

Abstract: Apparatuses, systems, and techniques to scale textured images using a Fourier transform in conjunction with one or more neural networks. In at least one embodiment, a neural network generates an expanded image from an input image by applying a Fourier transform to one or more feature maps generated by said neural network and up-scaling one or more resulting frequency domain feature maps before generating an expanded output image based on up-scaled feature maps.

Abstract: The disclosed method for generating a first depth map for a first frame of a video includes performing one or more operations to generate a first intermediate depth map based on the first frame and a second frame preceding the first frame within the video, performing one or more operations to generate a second intermediate depth map based on the first frame, and performing one or more operations to combine the first intermediate depth map and the second intermediate depth map to generate the first depth map.

Abstract: A device includes receiver logic to receive frames over a link, datalink logic to the receiver logic, to process the frames, and control logic coupled to the receiver logic and the datalink logic. The control logic is to determine whether a first frame indicates that a first subsequent frame is a low-power frame type, for which the datalink logic consumes less power than a second frame type, and cause the datalink logic to process the first subsequent frame as the low-power frame type by enabling a clock gate for at least a portion of the datalink logic used to process the low-power frame type, responsive to determining the first frame indicates that the first subsequent frame is the low-power frame type.

Abstract: In various examples, to support training a deep neural network (DNN) to predict a dense representation of a 3D surface structure of interest, a training dataset is generated using a parametric mathematical modeling. A variety of synthetic 3D road surfaces may be generated by modeling a 3D road surface using varied parameters to simulate changes in road direction and lateral surface slope. In an example embodiment, a synthetic 3D road surface may be created by modeling a longitudinal 3D curve and expanding the longitudinal 3D curve to a 3D surface, and the resulting synthetic 3D surface may be sampled to form a synthetic ground truth projection image (e.g., a 2D height map). To generate corresponding input training data, a known pattern that represents which pixels may remain unobserved during 3D structure estimation may be generated and applied to a ground truth projection image to simulate a corresponding sparse projection image.

Abstract: Apparatuses, systems, and techniques to perform a tensor prefetch instruction to cause one or more tensors to be transformed and stored into one or more caches. In at least one embodiment, one or more circuits of a GPU are to perform a tensor prefetch instruction to cause one or more tensors to be transformed and stored into one or more GPU caches.

Abstract: Apparatuses, systems, and techniques are presented to reconstruct one or more images. In at least one embodiment, one or more objects in an image are caused to be generated based, at least in part, on applying one or more offsets to a motion of the one or more objects relative to one or more prior images.

Abstract: Apparatuses, systems, and techniques to adjust one or more discontinuous wireless communication patterns. In at least one embodiment, a processor includes one or more circuits to use one or more neural networks to adjust one or more discontinuous wireless communication patterns.

Abstract: Apparatuses, systems, and techniques to provisioning of pipelines for efficient training, retraining, configuring, deploying, and using machine learning models for inference in user-specific platforms.

Abstract: Approaches presented herein provide systems and methods for generating three-dimensional (3D) objects using compressed data as an input. One or more models may learn from a hash table of latent features to map different features to a reconstruction domain, using a hash function as part of a learned process. A 3D shape for an object may be encoded to a multi-layered grid and represented by a series of embeddings, where given point within the grid may be interpolated based on the embeddings for a given layer of the multi-layered grid. A decoder may then be trained to use the embeddings to generate an output object.

Abstract: Apparatuses, systems, and techniques to train neural networks to perform image processing tasks. In at least one embodiment, one or more second neural networks are used to train one or more first neural networks based, at least in part, on a first object type in one or more images and a second object type in the one or more images, in parallel.

Abstract: In various examples, a visualization of an environment may be generated using a Panini projection that is optimized based on detected scene content. For example, image data of an environment may be perspective projected (e.g., using a rectilinear projection) to generate a reference projection image, which may be analyzed to detect the presence of vanishing points and/or horizontal lines (e.g., in a central region). The image data of the environment may be projected using a Panini projection that is optimized based on distances to detected objects, the absence of a detected vanishing point, and/or the presence of a detected horizontal line to generate a Panini projection image. In some embodiments, vertical compression is applied to the Panini projection image to correct for distortion of horizontal lines (e.g., based on the presence of a detected horizontal line).

Abstract: Apparatuses, systems, and techniques to use a schedule to cause wireless signal information to be transmitted. In at least one embodiment, a processor includes one or more circuits to use a schedule to cause wireless signal information to be transmitted repeatedly during a first period of time and to cause the wireless signal information to not be transmitted during a second period of time.

Abstract: This disclosure describes supporting distributed graphics and compute engines in a multi-dielet processor, such as, for example, a multi-dielet graphics processing unit (GPU), architectures and synchronization in such architectures. Each multi-dielet processor includes a hardware-implemented remapping capability and/or a hardware-implemented memory barrier capability.

Abstract: Apparatuses, systems, and techniques to train a neural network using varying levels of supervision. In at least one embodiment, a neural network is trained using a unified task head to facilitate supervision by both weak and strong methods of annotating input data.

Abstract: Apparatuses, systems, and techniques are presented to select segments of content. In at least one embodiment, one or more neural networks are used to select one or more video segments from one or more video files based at least in part upon an indication of one or more emotions, of one or more viewers of the one or more video segments, during presentation of the one or more video segments.

Abstract: In various examples, hybrid models for determining intents in conversational AI systems and applications are disclosed. Systems and methods are disclosed that use a machine learning model(s) and a data file(s) that associates intents with one another (e.g., using a tree-like structure) in order to determine a final intent associated with text. For example, the text may initially be processed using the machine learning model(s) (e.g., a first machine learning model) in order to determine a first intent associated with the text. The data file(s) may then be used to determine information (e.g., anchors) for one or more second intents (e.g., one or more sub-intents) that are related to the first intent. The text and the information may then be processed using the machine learning model(s) (e.g., a second machine learning model) to determine a second intent, from the one or more second intents, that is associated with the text.

Abstract: Systems and methods to support remote client access to server-based software development within a server that manages a container cluster, are disclosed. Exemplary implementations may launch one or more pods that may include sets of containers, including a first pod that executes a container management software application; launch a first set of containers including a first container; receive a connection instruction to establish a secure channel between a client computing platform and a remotely-accessible server-based software development environment (SDE) in the first container; establish the secure channel; receive user input for particular execution in the remotely-accessible server-based software development environment; perform the particular execution; and/or other actions.

Abstract: In various examples, metadata-based image harmonization for image stitching systems and applications are disclosed. Systems and methods are disclosed that preprocess images with respect to rendering parameters, with the effect of blending those parameters at a border between images to facilitate a smooth rendering when those images are stitched together. An image signal processing (ISP) parameter harmonization function may input metadata parameters associated with a set of images to match and blend one or more of the rendering parameters across an overlapping border between images prior to applying those images to a stitching algorithm. A scaling of the metadata parameter may be performed using a parameter gain function. Pixels in both images located along the border are adjusted to the same boundary metadata parameter value, and smoothed based on the parameter gain function. A discontinuity in rendering parameters is avoided, substantially avoiding corresponding artifacts in the resulting stitched image.