Abstract: A combined on-package and off-package memory system uses a custom base-layer within which are fabricated one or more dedicated interfaces to off-package memories. An on-package processor and on-package memories are also directly coupled to the custom base-layer. The custom base-layer includes memory management logic between the processor and memories (both off and on package) to steer requests. The memories are exposed as a combined memory space having greater bandwidth and capacity compared with either the off-package memories or the on-package memories alone. The memory management logic services requests while maintaining quality of service (QoS) to satisfy bandwidth requirements for each allocation. An allocation may include any combination of the on and/or off package memories. The memory management logic also manages data migration between the on and off package memories.

Abstract: Disclosed are apparatuses, systems, and techniques to perform and facilitate secure ladder computational operations whose iterative execution depends on secret values associated with input data. Disclosed embodiments balance execution of various iterations in a way that is balanced for different secret values, significantly reducing vulnerability of ladder computations to adversarial side-channel attacks.

Abstract: Apparatuses, systems, and techniques to compute cyclic redundancy checks use a graphics processing unit (GPU) to compute cyclic redundancy checks. For example, in at least one embodiment, an input data sequence is distributed among GPU threads for parallel calculation of an overall CRC value for the input data sequence according to various novel techniques described herein.

Abstract: The technology disclosed herein involves using a machine learning model (e.g., CNN) to expand lower dynamic-range image content (e.g., SDR images) into higher dynamic-range image content (e.g., HDR images). The machine learning model can take as input the lower dynamic-range image and can output multiple expansion maps that are used to make the expanded image appear more natural. The expansion maps may be used by image operators to smooth color banding and to dim overexposed regions or user interface elements in the expanded image. The expanded content (e.g., HDR image content) may then be provided to one or more devices for display or storage.

Abstract: In various examples, past location information corresponding to actors in an environment and map information may be applied to a deep neural network (DNN)—such as a recurrent neural network (RNN)—trained to compute information corresponding to future trajectories of the actors. The output of the DNN may include, for each future time slice the DNN is trained to predict, a confidence map representing a confidence for each pixel that an actor is present and a vector field representing locations of actors in confidence maps for prior time slices. The vector fields may thus be used to track an object through confidence maps for each future time slice to generate a predicted future trajectory for each actor. The predicted future trajectories, in addition to tracked past trajectories, may be used to generate full trajectories for the actors that may aid an ego-vehicle in navigating the environment.

Abstract: Apparatuses, systems, and techniques for handling faults by a direct memory access (DMA) engine. When a DMA engine detects an error associated with an encryption or decryption operation, the DMA engine reports the error to a CPU, which may be executing an untrusted software directing a DMA operation, and the secure processor. The DMA engine waits for clearance from the secure processor before responding to further directions from the potentially untrusted software.

Abstract: Apparatuses, systems, and techniques to estimate one or more wireless channels between one or more user devices and a base station. In at least one embodiment, one or more circuits use one or more groups of two or more reflected wireless reference signals to estimate the one or more wireless channels based, at least in part, on one or more bandlimited functions.

Abstract: A method, computer program product, apparatus, and system are provided. Some embodiments may include transmitting a request to make one or more writes associated with an identification tag. The request may include the identification tag, the one or more writes, a first instruction to make the one or more writes to one of a plurality of persistence levels of a memory, and a second instruction to respond with at least one first indication that the one or more writes associated with the identification tag have been written to at least one of the one of the plurality of persistence levels of the memory. Some embodiments may include receiving the at least one first indication that the one or more writes associated with the identification tag have been written to at least one of the one of the plurality of persistence levels of the memory.

Abstract: A neural network includes at least a first network layer that includes a first set of filters and a second network layer that includes a second set of filters. Notably, a filter was removed from the first network layer. A bias associated with a different filter included in the second set of filters compensates for a different bias associated with the filter that was removed from the first network layer.

Abstract: Systems and methods for cooling a datacenter are disclosed. In at least one embodiment, an integrated power and coolant distribution unit (PCDU) is provided to determine a change in a power state or a coolant state of at least one server and to enable a coolant response from an overhead cooling unit (OCU) to dissipate heat from secondary coolant of a secondary cooling loop.

Abstract: The disclosure is directed to a process that can predict and prevent an audio artifact from occurring. The process can monitor the systems, processes, and execution threads on a larger system/device, such as a mobile or in-vehicle device. Using a learning algorithm, such as deep neural network (DNN), the information collected can generate a prediction of whether an audio artifact is likely to occur. The process can use a second learning algorithm, which also can be a DNN, to generate recommended system adjustments that can attempt to prevent the audio glitch from occurring. The recommendations can be for various systems and components on the device, such as changing the processing system frequency, the memory frequency, and the audio buffer size. After the audio artifact has been prevented, the system adjustments can be reversed fully or in steps to return the system to its state prior to the system adjustments.

Abstract: One embodiment of a method includes predicting one or more three-dimensional (3D) mesh representations based on a plurality of digital images, wherein the one or more 3D mesh representations are refined by minimizing at least one difference between the one or more 3D mesh representations and the plurality of digital images.

Abstract: Apparatuses, systems, and techniques to route data transfers between hardware devices. In at least one embodiment, a path over which to transfer data from a first hardware component of a computer system to a second hardware component of a computer system is determined based, at least in part, on one or more characteristics of different paths usable to transfer the data.

Abstract: Approaches are presented for training and using scene graph generators for transfer learning. A scene graph generation technique can decompose a domain gap into individual types of discrepancies, such as may relate to appearance, label, and prediction discrepancies. These discrepancies can be reduced, at least in part, by aligning the corresponding latent and output distributions using one or more gradient reversal layers (GRLs). Label discrepancies can be addressed using self-pseudo-statistics collected from target data. Pseudo statistic-based self-learning and adversarial techniques can be used to manage these discrepancies without the need for costly supervision from a real-world dataset.

Abstract: In various examples, image areas may be extracted from a batch of one or more images and may be scaled, in batch, to one or more template sizes. Where the image areas include search regions used for localization of objects, the scaled search regions may be loaded into Graphics Processing Unit (GPU) memory and processed in parallel for localization. Similarly, where image areas are used for filter updates, the scaled image areas may be loaded into GPU memory and processed in parallel for filter updates. The image areas may be batched from any number of images and/or from any number of single- and/or multi-object trackers. Further aspects of the disclosure provide approaches for associating locations using correlation response values, for learning correlation filters in object tracking based at least on focused windowing, and for learning correlation filters in object tracking based at least on occlusion maps.

Abstract: In examples, the number of rays used to sample lighting conditions of a light source in a virtual environment with respect to particular locations in the virtual environment may be adapted to scene conditions. An additional ray(s) may be used for locations that tend to be associated with visual artifacts in rendered images. A determination may be made on whether to cast an additional ray(s) to a light source for a location and/or a quantity of rays to cast. To make the determination variables such as visibilities and/or hit distances of ray-traced samples of the light source may be analyzed for related locations in the virtual environment, such as those in a region around the location (e.g., within an N-by-N kernel centered at the location). Factors may include variability in visibilities and/or hit distances, differences between visibilities and/or hit distances relative to the location, and magnitudes of hit distances.

Abstract: A method dynamically selects one of a first sampling order and a second sampling order for a ray trace of pixels in a tile where the selection is based on a motion vector for the tile. The sampling order may be a bowtie pattern or an hourglass pattern.

Abstract: In various examples, systems and methods are disclosed relating to aligning images into frames of a first video using at least one first temporal attention layer of a neural network model. The first video has a first spatial resolution. A second video having a second spatial resolution is generated by up-sampling the first video using at least one second temporal attention layer of an up-sampler neural network model, wherein the second spatial resolution is higher than the first spatial resolution.

Abstract: Systems and methods for cooling in a datacenter are disclosed. In at least one embodiment, a thermal load bank (TLB) system to test a hybrid datacenter cooling system includes one or more thermal features to generate heat within a TLB system and includes one or more hybrid cooling features to provide air and liquid cooling responses to such heat generated by one or more thermal features.

Abstract: Systems and methods for cooling a computer environment are disclosed. In at least one embodiment, a cooling assembly can be used to monitor and control fluid quality associated with one or more servers.