Abstract: Described herein is a cloud-based gaming system in which graphics processing operations of a cloud-based game can be performed on a client device. Client-based graphics processing can be enabled in response to a determination that the client includes a graphics processor having a performance that exceeds a minimum threshold. When a game is remotely executed and streamed to a client, the client is configurable to provide network feedback that can be used to adjust execution and/or encoding for the game.

Abstract: Various systems and methods for content adaptation based on seat position or occupant position in a vehicle are described herein. An example implementation for content adaptation based on seat position in a vehicle includes: obtaining sensor data, the sensor data including a seat position of a seat in the vehicle; identifying audiovisual content for output to a human occupant in the vehicle; identifying an occupant position of the human occupant, based on the seat position, for a user experience of the output of the audiovisual content; and cause one or more adjustments to the output of the audiovisual content in the vehicle, via an output device, based on the identified position of the human occupant.

Abstract: An apparatus to facilitate disaggregated computing for a distributed confidential computing environment is disclosed. The apparatus includes a source remote direct memory access (RDMA) network interface controller (RNIC); a queue to store a data entry corresponding to an RDMA request between the source RNIC and a sink RNIC; a data buffer to store data for an RDMA transfer corresponding to the RDMA request, the RDMA transfer between the source RNIC and the sink RNIC; and a trusted execution environment (TEE) comprising an authentication tag controller to: initialize a first authentication tag calculated using a first key known between a source consumer generating the RDMA request and the source RNIC; associate the first authentication tag with the data entry as integrity verification; initialize a second authentication tag calculated using a second key; and associate the second authentication tag with the data buffer as integrity verification for the data buffer.

Abstract: An apparatus to facilitate disaggregated computing for a distributed confidential computing environment is disclosed. The apparatus includes one or more processors to facilitate receiving a manifest corresponding to graph nodes representing regions of memory of a remote client machine, the graph nodes corresponding to a command buffer and to associated data structures and kernels of the command buffer used to initialize a hardware accelerator and execute the kernels, and the manifest indicating a destination memory location of each of the graph nodes and dependencies of each of the graph nodes; identifying, based on the manifest, the command buffer and the associated data structures to copy to the host memory; identifying, based on the manifest, the kernels to copy to local memory of the hardware accelerator; and patching addresses in the command buffer copied to the host memory with updated addresses of corresponding locations in the host memory.

Abstract: An apparatus to facilitate disaggregated computing for a distributed confidential computing environment is disclosed. The apparatus includes a processor executing a trusted execution environment (TEE) comprising a field-programmable gate array (FPGA) driver to interface with an FPGA device that is remote to the apparatus; and a remote memory-mapped input/output (MMIO) driver to expose the FPGA device as a legacy device to the FPGA driver, wherein the processor to utilize the remote MMIO driver to: enumerate the FPGA device using FPGA enumeration data provided by a remote management controller of the FPGA device, the FPGA enumeration data comprising a configuration space and device details; load function drivers for the FPGA device in the TEE; create corresponding device files in the TEE based on the FPGA enumeration data; and handle remote MMIO reads and writes to the FPGA device via a network transport protocol.

Abstract: Various embodiments for dynamically generating an advertisement in a video stream are disclosed. In one embodiment, video stream content associated with a video stream for a user device is received. Video analytics data is obtained for the video stream content, which indicates a scene recognized in the video stream content. An advertisement to be generated and inserted into the video stream content is then selected based on the scene recognized in the video stream content, and an advertisement template for generating the selected advertisement is obtained. Video advertisement content corresponding to the advertisement is then generated based on the advertisement template and the video analytics data. The video advertisement content is then inserted into the video stream content, and the modified video stream content is transmitted to the user device.

Abstract: One embodiment provides a graphics processor comprising a block of execution resources, a cache memory, a cache memory prefetcher, and circuitry including a programmable neural network unit, the programmable neural network unit comprising a network hardware block including circuitry to perform neural network operations and activation operations for a layer of a neural network, the programmable neural network unit addressable by cores within the block of graphics cores and the neural network hardware block configured to perform operations associated with a neural network configured to determine a prefetch pattern for the cache memory prefetcher.

Abstract: Apparatuses, methods, and storage medium associated with a browser for prioritized display of videos and/or photographs are disclosed herein. In embodiments, an apparatus may include one or more processors, devices, and/or circuitry to operate a browser to present a plurality of photos and/or videos for viewing. A subset of the plurality of the photos and/or videos may be selected based on the results of an analysis of sensor data collected by a plurality of wearable sensors. The subset of the plurality of the photos and/or videos may be prioritized over other photos and/or videos from the plurality of the photos and/or videos in terms of presentation space allocated for presentation.

Abstract: A system for detecting malware includes a processor to collect processor trace information corresponding to an application being executed by the processor (202). The processor can also detect an invalid indirect branch instruction from the processor trace information (204) and detect at least one malware instruction being executed by the application in response to analyzing modified memory values corresponding to the invalid indirect branch (206). Additionally, the processor can block the application from accessing or modifying memory (208).

Abstract: One embodiment provides for a graphics processor comprising a block of graphics compute units, a graphics processor pipeline coupled to the block of graphics compute units, and a programmable neural network unit including one or more neural network hardware blocks. The programmable neural network unit is coupled with the block of graphics compute units and the graphics processor pipeline. The one or more neural network hardware blocks include hardware to perform neural network operations and activation operations for a layer of a neural network. The programmable neural network unit can configure settings of one or more hardware blocks within the graphics processor pipeline based on a machine learning model trained to optimize performance of a set of workloads.

Abstract: A method includes receiving a request for a transfer of data on a bus of a computing device; determining a direction for the transfer, at least in part based on the request; determining a quantity of data for the transfer, at least in part based on the request; determining a power state for a lane of the bus, at least in part based on the direction and the quantity of data for the transfer; and setting the power state for the lane of the bus.

Abstract: An apparatus and method for scheduling threads on local and remote processing resources.

Abstract: Embodiments are directed to neural network processing for multi-object three-dimensional (3D) modeling. An embodiment of a computer-readable storage medium includes executable computer program instructions for obtaining data from multiple cameras, the data including multiple images, and generating a 3D model for 3D imaging based at least in part on the data from the cameras, wherein generating the 3D model includes one or more of performing processing with a first neural network to determine temporal direction based at least in part on motion of one or more objects identified in an image of the multiple images or performing processing with a second neural network to determine semantic content information for an image of the multiple images.

Abstract: An apparatus to facilitate video motion smoothing is disclosed. The apparatus comprises one or more processors including a graphics processor, the one or more processors including circuitry configured to receive a video stream, decode the video stream to generate a motion vector map and a plurality of video image frames, analyze the motion vector map to detect a plurality of candidate frames, wherein the plurality of candidate frames comprise a period of discontinuous motion in the plurality of video image frames and the plurality of candidate frames are determined based on a classification generated via a convolutional neural network (CNN), generate, via a generative adversarial network (GAN), one or more synthetic frames based on the plurality of candidate frames, insert the one or more synthetic frames between the plurality of candidate frames to generate up-sampled video frames and transmit the up-sampled video frames for display.

Abstract: An apparatus to facilitate deep learning based selection of samples for adaptive supersampling is disclosed. The apparatus includes one or more processing elements to: receive training data comprising input tiles and corresponding supersampling values for the input tiles, wherein each input tile comprises a plurality of pixels, and train, based on the training data, a machine learning model to identify a level of supersampling for a rendered tile of pixels.

Abstract: Examples disclosed herein provide real-time language learning within a smart space. An example system includes a sensor; object detection software to identify a first object and a second object in an environment based on an output of the sensor; assign a first weight to the first object and a second weight to the second object; perform a comparison of the first weight and the second weight; and select the first object to be associated with a second language output based on the comparison; context determination software to determine a second language context based on the output of the sensor; linguistic analysis software to associate the first object with a second language based on the second language context; and prompt generation software to cause the second language output for the first object in the second language to be presented.

Abstract: One embodiment provides a method comprising, at a runtime library executed by a processor of a data processing system, receiving an input frame having objects to be stylized via a style transfer network associated with the runtime library, wherein the style transfer network is a neural network model trained to apply one or more visual styles to an input frame, performing instance segmentation on the input frame to generate one or more instance masks to identify one or more objects to be stylized, generating one or more stylized frames for each style to transfer to the input frame, and merging, via the one or more instance masks, stylized objects from one or more stylized frames with un-stylized content from the input frame to generate an output frame with per-instance stylization.

Abstract: Language education systems capable of integrating with a user's daily life and automatically producing educational prompts would be particularly advantageous. An example method includes determining a user's identity, detecting a language education subject, prompting the user with a language education message, receiving a user's response, and updating a user profile associated with the user based on the user's response. Methods may also include determining user state (including emotional, physical, social, etc.) and determining, based on the user state, whether to prompt the user with the language education prompt.

Abstract: An apparatus and method for scheduling threads on local and remote processing resources.

Abstract: Embodiments described herein include software, firmware, and hardware that provides techniques to enable deterministic scheduling across multiple general-purpose graphics processing units. One embodiment provides a multi-GPU architecture with uniform latency. One embodiment provides techniques to distribute memory output based on memory chip thermals. One embodiment provides techniques to enable thermally aware workload scheduling. One embodiment provides techniques to enable end to end contracts for workload scheduling on multiple GPUs.