Abstract: Methods of forming a graphene integrated core for making a printed circuit board (PCB) having enhanced thermal management properties are disclosed. The methods include providing a core body having a core body length and applying a graphene multi-layer to the core body to form a laminated stack, where the graphene multi-layer has a graphene multi-layer length that is shorter than the core body length. At least one conductive layer may be applied to the laminated stack. The graphene multi-layer may be disposed within the graphene integrated core such that the graphene multi-layer is electrically insulated from the at least one conductive layer. Corresponding graphene integrated cores having a graphene multi-layer that is disposed within the graphene integrated core such that the graphene multi-layer is electrically insulated from the at least one conductive layer are also described.

Abstract: Methods for integrating copper-graphene laminate (CGL) in a multilayer PCB fabrication process and the resulting lamination stacks are disclosed. The methods include providing a core and applying a first graphene layer to the surface of the core. The methods further include applying a metal layer to the first graphene layer and applying a second graphene layer to the metal layer. Further, the methods include applying a photoresist layer to the second graphene layer and applying a protective layer to the photoresist layer. In some embodiments, the methods include applying a metallic plating to lamination stack. The methods further include drilling through the protective layer and at least one of a photoresist layer, the second graphene layer, the metal layer, the first graphene layer, and/or the core.

Abstract: In various examples, live perception from wide-view sensors may be leveraged to detect features in an environment of a vehicle. Sensor data generated by the sensors may be adjusted to represent a virtual field of view different from an actual field of view of the sensor, and the sensor data—with or without virtual adjustment—may be applied to a stereographic projection algorithm to generate a projected image. The projected image may then be applied to a machine learning model—such as a deep neural network (DNN)—to detect and/or classify features or objects represented therein. In some examples, the machine learning model may be pre-trained on training sensor data generated by a sensor having a field of view less than the wide-view sensor such that the virtual adjustment and/or projection algorithm may update the sensor data to be suitable for accurate processing by the pre-trained machine learning model.

Abstract: Systems and methods for evaluating cooling characteristics are disclosed. In at least one embodiment, a thermal testing rig for a data center can include one or more pluggable heat-generating elements to direct a heat flux in an upward direction towards one or more thermal distribution elements.

Abstract: User interfaces, methods and structures are described for intuitively and fluidly creating new artifacts from existing artifacts and for exploring latent spaces in a visual manner. In example embodiments, source artifacts are displayed along with a selector. The selector is operable to indicate a selected set of the source artifacts by establishing a selection region that includes portions of one or more of the source artifacts displayed. Source vectors are associated with the source artifacts in the selected set. One or more resultant vectors are determined based on the source vectors, and an output artifact is generated based on the one or more resultant vectors.

Abstract: Apparatuses, systems, and techniques to generate blue noise masks for real-time image rendering and enhancement. In at least one embodiment, a vector-valued noise mask is generated and applied to one or more images to generate one or more enhanced images for image processing (e.g., real-time image rendering). In at least one embodiment, the noise mask includes vector values per pixel and is able to handle the temporal domain (e.g., add time to the spatial domain) to improve image quality when rendering images over multiple frames.

Abstract: Apparatuses, systems, and techniques to perform a cryptographic operation using multiple iterations, wherein each iteration includes two or more stages operating in parallel on inputs derived from a common value, one of the stages computing real data and other stages computing dummy data.

Abstract: An alternate root tree or graph structure for ray and path tracing enables dynamic instancing build time decisions to split any number of geometry acceleration structures in a manner that is developer transparent, nearly memory storage neutral, and traversal efficient. The resulting traversals only need to partially traverse the acceleration structure, which improves efficiency. One example use reduces the number of false positive instance acceleration structure to geometry acceleration structure transitions for many spatially separated instances of the same geometry.

Abstract: In various examples, a neural network may be trained for use in vehicle re-identification tasks—e.g., matching appearances and classifications of vehicles across frames—in a camera network. The neural network may be trained to learn an embedding space such that embeddings corresponding to vehicles of the same identify are projected closer to one another within the embedding space, as compared to vehicles representing different identities. To accurately and efficiently learn the embedding space, the neural network may be trained using a contrastive loss function or a triplet loss function. In addition, to further improve accuracy and efficiency, a sampling technique—referred to herein as batch sample—may be used to identify embeddings, during training, that are most meaningful for updating parameters of the neural network.

Abstract: In various examples, 3D object knowledge can be developed to extract diverse knowledge from large language models, and a part-grounding model can be trained to ground part semantics in terms of local shape features and spatial relations between parts. For example, knowledge that “the opening part of a mug that affords the pouring action is located on the top of the mug body and is often circular” can be grounded by identifying a previously unknown “opening” part based on its spatial relation to the known “body” part and its circular shape. A robotic system, for example, may use a model to identify an unlabeled part of a 3D object in imaging data. The model may be generated using natural language descriptions of relationships between parts of 3D objects, with descriptions generated using a language model that produces text in response to queries related to spatial relationships between the parts.

Abstract: In various examples, systems and methods are disclosed relating to using neural networks for object detection or instance/semantic segmentation for, without limitation, autonomous or semi-autonomous systems and applications. In some implementations, one or more neural networks receive an image (or other sensor data representation) and a bounding shape corresponding to at least a portion of an object in the image. The bounding shape can include or be labeled with an identifier, class, and/or category of the object. The neural network can determine a mask for the object based at least on processing the image and the bounding shape. The mask can be used for various applications, such as annotating masks for vehicle or machine perception and navigation processes.

Abstract: In various examples, a 3D surface structure such as the 3D surface structure of a road (3D road surface) may be observed and estimated to generate a 3D point cloud or other representation of the 3D surface structure. Since the representation may be sparse, one or more densification techniques may be applied to densify the representation of the 3D surface structure. For example, the relationship between sparse and dense projection images (e.g., 2D height maps) may be modeled with a Markov random field, and Maximum a Posterior (MAP) inference may be performed using a corresponding joint probability distribution to estimate the most likely dense values given the sparse values. The resulting dense representation of the 3D surface structure may be provided to an autonomous vehicle drive stack to enable safe and comfortable planning and control of the autonomous vehicle.

Abstract: A method, computer readable medium, and system are disclosed for overlaying a cell onto a polygon meshlet. The polygon meshlet may include a grouping of multiple geometric shapes such as triangles, and the cell may include a square-shaped boundary. Additionally, every polygon (e.g., a triangle or other geometric shape) within the polygon meshlet that has at least one edge fully inside the cell is removed to create an intermediate meshlet. A selected vertex is determined from all vertices (e.g., line intersections) of the intermediate meshlet that are located within the cell, based on one or more criteria, and all the vertices of the intermediate meshlet that are located within the cell are replaced with the selected vertex to create a modified meshlet. The modified meshlet is then rendered (e.g., as part of a process to generate a scene to be viewed).

Abstract: Storage processing units or SPUs (120) operate backend storage (150) to provide scalable storage services, redundancy, and disaster recovery to an enterprise. Each SPU (120) may reside in a host server (110) and may include an processor domain (490) with backup power (440) and isolation from a host domain (480) to allow the SPU (120) to operate after the host (110) fails or otherwise stops providing power. A cloud-based management system (180) may assess the storage needs of the enterprise, identify a storage style suited to the enterprise, and direct the SPUs (120) to create virtual volumes (122, 124, 128) having characteristics according to the storage style identified. The cloud based management system (180) may eliminate the need for the enterprise to have expertise in storage management.

Abstract: A hardware-based traversal coprocessor provides acceleration of tree traversal operations searching for intersections between primitives represented in a tree data structure and a ray. The primitives may include opaque and alpha triangles used in generating a virtual scene. The hardware-based traversal coprocessor is configured to determine primitives intersected by the ray, and return intersection information to a streaming multiprocessor for further processing. The hardware-based traversal coprocessor is configured to omit reporting of one or more primitives the ray is determined to intersect. The omitted primitives include primitives which are provably capable of being omitted without a functional impact on visualizing the virtual scene.

Abstract: In various examples, machine learning of encoding parameter values for a network is performed using a video encoder. Feedback associated with streaming video encoded by a video encoder over a network may be applied to an MLM(s). Using such feedback, the MLM(s) may predict a value(s) of an encoding parameter(s). The video encoder may then use the value to encode subsequent video data for the streaming. By using the video encoder in training, the MLM(s) may learn based on actual encoded parameter values of the video encoder. The MLM(s) may be trained via reinforcement learning based on video encoded by the video encoder. A rewards metric(s) may be used to train the MLM(s) using data generated or applied to the physical network in which the MLM(s) is to be deployed and/or a simulation thereof. Penalty metric(s) (e.g., the quantity of dropped frames) may also be used to train the MLM(s).

Abstract: A memory device and a system that implements a single symbol correction, double symbol detection (SSC-DSD+) error correction scheme are provided. The scheme is implemented by calculating four syndrome symbols in accordance with a Reed-Solomon (RS) codeword; determining three location bytes in accordance with three corresponding pairs of syndrome symbols in the four syndrome symbols; and generating an output based on a comparison of the three location bytes. The output may include: corrected data responsive to determining that the three location bytes match; an indication of a detected-and-corrected error (DCE) responsive to determining that two of the three location bytes match; or an indication of a detected-yet-uncorrected error (DUE) responsive to determining that none of the three location bytes match. A variant of the SSC-DSD+ decoder may be implemented using a carry-free subtraction operation to perform sanity checking.

Abstract: Systems and methods for cooling a datacenter are disclosed. In at least one embodiment, a number of multi-dimensional column-based heat dissipation features enable cooling by a cooling media flowing there through so that an individual heat dissipation column having a first dimension and a second dimension may be supported, with the first dimension being normal relative to an axial flow path of the cooling media, with the second dimension being parallel or offset from parallel relative to the axial flow path and with the second dimension being more than the first dimension.

Abstract: Apparatuses, systems, and techniques to grasp objects with a robot. In at least one embodiment, a neural network is trained to determine a grasp pose of an object within a cluttered scene using a point cloud generated by a depth camera.

Abstract: 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.