Abstract: In an example, an apparatus comprises logic, at least partially comprising hardware logic, to receive an input from one or more detectors proximate a display to present an output from a graphics pipeline, determine that a user is not interacting with the display, and in response to a determination that the user is not interacting with the display, to reduce a frame rendering rate of the graphics pipeline. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises logic, at least partially comprising hardware logic, to receive an input from one or more detectors proximate a display to present an output from a graphics pipeline, determine that a user is not interacting with the display, and in response to a determination that the user is not interacting with the display, to reduce a frame rendering rate of the graphics pipeline. Other embodiments are also disclosed and claimed.

Abstract: Embodiments provide mechanisms to facilitate compute operations for deep neural networks. One embodiment comprises a graphics processing unit comprising one or more multiprocessors, at least one of the one or more multiprocessors including a register file to store a plurality of different types of operands and a plurality of processing cores. The plurality of processing cores includes a first set of processing cores of a first type and a second set of processing cores of a second type. The first set of processing cores are associated with a first memory channel and the second set of processing cores are associated with a second memory channel.

Abstract: In an example, an apparatus comprises logic, at least partially comprising hardware logic, to power on a first set of processing clusters, dispatch a workload to the first set of processing clusters, detect a full operating state of the first set of processing clusters, and in response to the detection of a full operating state of the first set of processing clusters, to power on a second set of processing clusters. Other embodiments are also disclosed and claimed.

Abstract: Embodiments provide mechanisms to facilitate compute operations for deep neural networks. One embodiment comprises a graphics processing unit comprising one or more multiprocessors, at least one of the one or more multiprocessors including a register file to store a plurality of different types of operands and a plurality of processing cores. The plurality of processing cores includes a first set of processing cores of a first type and a second set of processing cores of a second type. The first set of processing cores are associated with a first memory channel and the second set of processing cores are associated with a second memory channel.

Abstract: In an example, an apparatus comprises logic, at least partially comprising hardware logic, to power on a first set of processing clusters, dispatch a workload to the first set of processing clusters, detect a full operating state of the first set of processing clusters, and in response to the detection of a full operating state of the first set of processing clusters, to power on a second set of processing clusters. Other embodiments are also disclosed and claimed.

Abstract: Embodiments provide mechanisms to facilitate compute operations for deep neural networks. One embodiment comprises a graphics processing unit comprising one or more multiprocessors, at least one of the one or more multiprocessors including a register file to store a plurality of different types of operands and a plurality of processing cores. The plurality of processing cores includes a first set of processing cores of a first type and a second set of processing cores of a second type. The first set of processing cores are associated with a first memory channel and the second set of processing cores are associated with a second memory channel.

Abstract: In an example, an apparatus comprises a plurality of execution units comprising at least a first type of execution unit and a second type of execution unit and logic, at least partially including hardware logic, to analyze a workload and assign the workload to one of the first type of execution unit or the second type of execution unit. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of execution units comprising at least a first type of execution unit and a second type of execution unit and logic, at least partially including hardware logic, to analyze a workload and assign the workload to one of the first type of execution unit or the second type of execution unit. Other embodiments are also disclosed and claimed.

Abstract: Embodiments provide mechanisms to facilitate compute operations for deep neural networks. One embodiment comprises a graphics processing unit comprising one or more multiprocessors, at least one of the one or more multiprocessors including a register file to store a plurality of different types of operands and a plurality of processing cores. The plurality of processing cores includes a first set of processing cores of a first type and a second set of processing cores of a second type. The first set of processing cores are associated with a first memory channel and the second set of processing cores are associated with a second memory channel.

Abstract: Embodiments provide mechanisms to facilitate compute operations for deep neural networks. One embodiment comprises a graphics processing unit comprising one or more multiprocessors, at least one of the one or more multiprocessors including a register file to store a plurality of different types of operands and a plurality of processing cores. The plurality of processing cores includes a first set of processing cores of a first type and a second set of processing cores of a second type. The first set of processing cores are associated with a first memory channel and the second set of processing cores are associated with a second memory channel.

Abstract: An apparatus to facilitate compute optimization is disclosed. The apparatus includes one or more processing units to provide a first set of shader operations associated with a shader stage of a graphics pipeline, a scheduler to schedule shader threads for processing, and a field-programmable gate array (FPGA) dynamically configured to provide a second set of shader operations associated with the shader stage of the graphics pipeline.

Abstract: An embodiment of a graphics apparatus may include a context engine to determine contextual information, a recommendation engine communicatively coupled to the context engine to determine a recommendation based on the contextual information, and a configuration engine communicatively coupled to the recommendation engine to adjust a configuration of a graphics operation based on the recommendation. Other embodiments are disclosed and claimed.

Abstract: Embodiments provide mechanisms to facilitate compute operations for deep neural networks. One embodiment comprises a graphics processing unit comprising one or more multiprocessors, at least one of the one or more multiprocessors including a register file to store a plurality of different types of operands and a plurality of processing cores. The plurality of processing cores includes a first set of processing cores of a first type and a second set of processing cores of a second type. The first set of processing cores are associated with a first memory channel and the second set of processing cores are associated with a second memory channel.

Abstract: An apparatus and method are described for allocating local memories to virtual machines. For example, one embodiment of an apparatus comprises: a command streamer to queue commands from a plurality of virtual machines (VMs) or applications, the commands to be distributed from the command streamer and executed by graphics processing resources of a graphics processing unit (GPU); a tile cache to store graphics data associated with the plurality of VMs or applications as the commands are executed by the graphics processing resources; and tile cache allocation hardware logic to allocate a first portion of the tile cache to a first VM or application and a second portion of the tile cache to a second VM or application; the tile cache allocation hardware logic to further allocate a first region in system memory to store spill-over data when the first portion of the tile cache and/or the second portion of the file cache becomes full.

Abstract: Methods and apparatus relating to techniques for power management. In an example, an apparatus comprises logic, at least partially comprising hardware logic, to receive one or more frames for a workload, determine one or more compute resource parameters for the workload, and store the one or more compute resource parameters for the workload in a memory in association with workload context data for the workload. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of execution units comprising at least a first type of execution unit and a second type of execution unit and logic, at least partially including hardware logic, to analyze a workload and assign the workload to one of the first type of execution unit or the second type of execution unit. Other embodiments are also disclosed and claimed.

Abstract: An apparatus to facilitate compute optimization is disclosed. The apparatus includes a plurality of processing units each comprising a plurality of execution units (EUs), wherein the plurality of EUs comprise a first EU type and a second EU type.

Abstract: An apparatus to facilitate compute optimization is disclosed. The apparatus includes one or more processing units to provide a first set of shader operations associated with a shader stage of a graphics pipeline, a scheduler to schedule shader threads for processing, and a field-programmable gate array (FPGA) dynamically configured to provide a second set of shader operations associated with the shader stage of the graphics pipeline.

Abstract: An apparatus to facilitate compute optimization is disclosed. The apparatus includes a plurality of processing units each comprising a plurality of processing cores of a first type and a second type. A first set of processing cores of a first type perform multi-dimensional matrix operations and a second set of processing cores of a second type perform general purpose graphics processing unit (GPGPU) operations.