Abstract: Technologies for providing hints for adjusting digital media properties include a destination computing device wirelessly coupled to multiple source computing devices. The destination computing device is configured to receive digital media streams from each of a multiple number of source computing devices, process each of the received digital media streams, and output one or more of the processed digital media streams based on one or more output settings and/or or more digital media properties of the digital media. The destination computing device is further configured to determine one or more performance metrics based on an analysis of the output digital media streams, determine one or more hints for one or more of the digital media streams based on the analysis, and transmit each of the hints to a corresponding one of the source computing devices. Other embodiments are described and claimed herein.

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: Systems, apparatuses, methods, and computer-readable media, are provided for providing connectivity-based and/or connectivity-considered routing with supplemental wireless connections in driving assistance-related activities. Embodiments may be relevant to multi-access edge computing (MEC) and Automotive Edge Computing Consortium (AECC) technologies. Other embodiments may be described and/or claimed.

Abstract: Technologies for context-aware dynamic bandwidth allocation include a network compute device configured to collect context inputs from a plurality of compute devices communicatively coupled to the network compute device. The network compute device is further configured to identify a context of each compute device based on the collected context inputs and determine a bandwidth priority for each compute device based on the identified context. Additionally, the network compute device is configure to determine an amount of bandwidth from a total available bandwidth to allocate to the compute device based on the determined bandwidth priority and update a moderated bandwidth allocation policy to reflect the determined amount of bandwidth allocated to the compute device. Other embodiments are described herein.

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: Computers for supporting multiple virtual reality (VR) display devices and related methods are described herein. An example computer includes a graphics processing unit (GPU) to render frames for a first VR display device and a second VR display device, a memory to store frames rendered by the GPU for the first VR display device and the second VR display device, and a vertical synchronization (VSYNC) scheduler to transmit alternating first and second VSYNC signals to the GPU such that a time period between each of the first or second VSYNC signals and a subsequent one of the first or second VSYNC signals is substantially the same. The GPU is to, based on the first and second VSYNC signals, alternate between rendering a frame for the first VR display device and a frame for the second VR display device.

Abstract: Systems, apparatuses, and methods may provide for multi-session encoding to optimize multiple encoding sessions on Wi-Fi display (WFD) source devices when the WFD source devices are connected to multiple sink devices. The multiple encoding sessions may be optimized with encoding hints that are generated by a compositor and transmitted to a pre-encoding checking device. The encoding session that has the highest encoding resolution is subjected to hierarchical motion estimation (HME) processing, and the encoding sessions that have lower resolutions are optimized based on a motion vector prediction hint generated by the encoding session that has the highest encoding resolution and a scaling factor.

Abstract: Technologies for streaming device role reversal include a source computing device and a destination computing device coupled via a communication channel. The source computing device and destination computing device are each configured to support role reversal. In other words, the source computing device and the destination computing device are each capable of switching between receiving and transmitting digital media content over the established communication channel. The source computing device is configured to initiate the role reversal, pause transmit functionality of the source computing device, and enable receive functionality of the source computing device. The destination computing device is configured to receive a role reversal indication from the source computing device, locally process the content, transmit a content stream to the source computing device, and display the content stream on an output device of the source computing device. Other embodiments are described and claimed herein.

Abstract: Technologies for casting digital media content include a source computing device communicatively coupled to a destination computing device. The source computing device is configured to transmit a set of identifiers of the source computing device to the destination computing device and receive a set of identifiers of the destination computing device from the destination computing device. Each set of identifiers includes a version (e.g., hardware or software) associated with the respective computing device and a unique identifier of the respective computing device. The source computing device is additionally configured to determine a subset of casting session parameters from a set of casting session parameters based on the one or more capabilities of the destination computing device which are usable by the destination computing device to establish a casting session and stream digital media content in accordance with the subset of casting session parameters. Other embodiments are described and claimed.

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: Technologies for end of frame marking and detection in streaming digital media content include a source computing device communicatively coupled to a destination computing device. The source computing device is configured to encode a frame of digital media content and insert an end of frame marker into a transport stream header of a network packet that includes an encoded payload corresponding to a chunk of data of the frame of digital media content. The destination computing device is configured to de-packetize received network packets and parse the transport stream headers of the received network packets to determine whether the network packet corresponds to an end of frame of the frame of digital media content. The destination computing device is further configured to transmit the encoded payloads of the received network packets to a decoder in response to a determination that the end of frame network packet has been received. Other embodiments are described and claimed.

Abstract: Technologies for providing hints for adjusting digital media properties include a destination computing device wirelessly coupled to multiple source computing devices. The destination computing device is configured to receive digital media streams from each of a multiple number of source computing devices, process each of the received digital media streams, and output one or more of the processed digital media streams based on one or more output settings and/or or more digital media properties of the digital media. The destination computing device is further configured to determine one or more performance metrics based on an analysis of the output digital media streams, determine one or more hints for one or more of the digital media streams based on the analysis, and transmit each of the hints to a corresponding one of the source computing devices. Other embodiments are described and claimed herein.

Abstract: Technologies for streaming device role reversal include a source computing device and a destination computing device coupled via a communication channel. The source computing device and destination computing device are each configured to support role reversal. In other words, the source computing device and the destination computing device are each capable of switching between receiving and transmitting digital media content over the established communication channel. The source computing device is configured to initiate the role reversal, pause transmit functionality of the source computing device, and enable receive functionality of the source computing device. The destination computing device is configured to receive a role reversal indication from the source computing device, locally process the content, transmit a content stream to the source computing device, and display the content stream on an output device of the source computing device. Other embodiments are described and claimed herein.

Abstract: Technologies for casting digital media content include a source computing device communicatively coupled to a destination computing device. The source computing device is configured to transmit a set of identifiers of the source computing device to the destination computing device and receive a set of identifiers of the destination computing device from the destination computing device. Each set of identifiers includes a version (e.g., hardware or software) associated with the respective computing device and a unique identifier of the respective computing device. The source computing device is additionally configured to determine a subset of casting session parameters from a set of casting session parameters based on the one or more capabilities of the destination computing device which are usable by the destination computing device to establish a casting session and stream digital media content in accordance with the subset of casting session parameters. Other embodiments are described and claimed.

Abstract: Technologies for providing hints for adjusting digital media properties include a destination computing device wirelessly coupled to multiple source computing devices. The destination computing device is configured to receive digital media streams from each of a multiple number of source computing devices, process each of the received digital media streams, and output one or more of the processed digital media streams based on one or more output settings and/or or more digital media properties of the digital media. The destination computing device is further configured to determine one or more performance metrics based on an analysis of the output digital media streams, determine one or more hints for one or more of the digital media streams based on the analysis, and transmit each of the hints to a corresponding one of the source computing devices. Other embodiments are described and claimed herein.

Abstract: Systems, apparatuses, methods, and computer-readable media, are provided for providing connectivity-based and/or connectivity-considered routing with supplemental wireless connections in driving assistance-related activities. Embodiments may be relevant to multi-access edge computing (MEC) and Automotive Edge Computing Consortium (AECC) technologies. Other embodiments may be described and/or claimed.

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: Systems, apparatuses, and methods may provide for multi-session encoding to optimize multiple encoding sessions on Wi-Fi display (WFD) source devices when the WFD source devices are connected to multiple sink devices. The multiple encoding sessions may be optimized with encoding hints that are generated by a compositor and transmitted to a pre-encoding checking device. The encoding session that has the highest encoding resolution is subjected to hierarchical motion estimation (HME) processing, and the encoding sessions that have lower resolutions are optimized based on a motion vector prediction hint generated by the encoding session that has the highest encoding resolution and a scaling factor.

Abstract: Technologies for casting digital media content include a source computing device communicatively coupled to a destination computing device. The source computing device is configured to transmit a set of identifiers of the source computing device to the destination computing device and receive a set of identifiers of the destination computing device from the destination computing device. Each set of identifiers includes a version (e.g., hardware or software) associated with the respective computing device and a unique identifier of the respective computing device. The source computing device is additionally configured to determine a subset of casting session parameters from a set of casting session parameters based on the one or more capabilities of the destination computing device which are usable by the destination computing device to establish a casting session and stream digital media content in accordance with the subset of casting session parameters. Other embodiments are described and claimed.

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.