Abstract: Systems, apparatuses and methods may provide for technology that receives, at a topology shader in a graphics pipeline, an object description and generates, at the topology shader, a set of polygons based on the object description. Additionally, the set of polygons may be sent to a vertex shader.

Abstract: In an example, an apparatus comprises logic, at least partially including hardware logic, to implement a lossy compression algorithm which utilizes a data transform and quantization process to compress data in a convolutional neural network (CNN) layer. Other embodiments are also disclosed and claimed.

Abstract: An embodiment of a graphics apparatus may include a mask buffer to store a mask, a shader communicatively coupled to the mask buffer to apply the mask to a first shader pass, and a resolver communicatively coupled to the mask buffer to apply the mask to a resolve pass. The resolver may be configured to exclude a sample location not covered by the mask in the resolve pass. Other embodiments are disclosed and claimed.

Abstract: An embodiment of an electronic processing system may include an application processor, persistent storage media communicatively coupled to the application processor, a graphics subsystem communicatively coupled to the application processor, a sense engine communicatively coupled to the graphics subsystem to provide sensed information, a focus engine communicatively coupled to the sense engine and the graphics subsystem to provide focus information, a motion engine communicatively coupled to the sense engine, the focus engine, and the graphics subsystem to provide motion information, and a motion biased foveated renderer communicatively coupled to the motion engine, the focus engine, the sense engine to adjust one or more parameters of the graphics subsystem based on one or more of the sense information, the focus information, and the motion information. Other embodiments are disclosed and claimed.

Abstract: An apparatus to facilitate compute optimization is disclosed. The apparatus includes a at least one processor to perform operations to implement a neural network and compute logic to accelerate neural network computations.

Abstract: In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to receive a plurality of data inputs for training a neural network, wherein the data inputs comprise training data and weights inputs; represent the data inputs in a first form; and represent the weight inputs in a second form. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to traverse a solution space, score a plurality of solutions to a scheduling deep learning network execution, and select a preferred solution from the plurality of solutions to implement the deep learning network. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises logic, at least partially including hardware logic, to implement a lossy compression algorithm which utilizes a data transform and quantization process to compress data in a convolutional neural network (CNN) layer. Other embodiments are also disclosed and claimed.

Abstract: An embodiment of a graphics apparatus may include a mask buffer to store a mask, a shader communicatively coupled to the mask buffer to apply the mask to a first shader pass, and a resolver communicatively coupled to the mask buffer to apply the mask to a resolve pass. The resolver may be configured to exclude a sample location not covered by the mask in the resolve pass. Other embodiments are disclosed and claimed.

Abstract: An apparatus to facilitate compute optimization is disclosed. The apparatus includes a at least one processor to perform operations to implement a neural network and compute logic to accelerate neural network computations.

Abstract: In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to traverse a solution space, score a plurality of solutions to a scheduling deep learning network execution, and select a preferred solution from the plurality of solutions to implement the deep learning network. Other embodiments are also disclosed and claimed.

Abstract: Systems, apparatuses and methods may provide for technology that receives, at a topology shader in a graphics pipeline, an object description and generates, at the topology shader, a set of polygons based on the object description. Additionally, the set of polygons may be sent to a vertex shader.

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 expose embedded cast operations in at least one of a load instruction or a store instruction; determine a target precision level for the cast operations; and load the cast operations at the target precision level. 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 expose embedded cast operations in at least one of a load instruction or a store instruction; determine a target precision level for the cast operations; and load the cast operations at the target precision level. Other embodiments are also disclosed and claimed.

Abstract: An apparatus comprising an input stage comprising a first input, a second input, a first AC coupler, and a second AC coupler. The first AC coupler is coupled between the first input and a third input. The second AC coupler is coupled between the second input and a fourth input. A comparator coupled to the third input, the fourth input, and an output. The comparator provides an output signal at the output based on a comparison between a level of a first voltage at the third input, and a level of a second voltage at the fourth input. A feedback circuit, coupled to the output, the third input, and the fourth input. The feedback circuit receives the output signal, and provides, based on the output signal, a first feedback voltage to the third input.

Abstract: In an example, an apparatus comprises a plurality of execution units, and a first memory communicatively couple to the plurality of execution units, wherein the first shared memory is shared by the plurality of execution units and a copy engine to copy context state data from at least a first of the plurality of execution units to the first shared memory. Other embodiments are also disclosed and claimed.

Abstract: An embodiment of a graphics apparatus may include a mask buffer to store a mask, a shader communicatively coupled to the mask buffer to apply the mask to a first shader pass, and a resolver communicatively coupled to the mask buffer to apply the mask to a resolve pass. The resolver may be configured to exclude a sample location not covered by the mask in the resolve pass. Other embodiments are disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to receive a plurality of data inputs for training a neural network, wherein the data inputs comprise training data and weights inputs; represent the data inputs in a first form; and represent the weight inputs in a second form. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises logic, at least partially including hardware logic, to implement a lossy compression algorithm which utilizes a data transform and quantization process to compress data in a convolutional neural network (CNN) layer. Other embodiments are also disclosed and claimed.

Abstract: A fully isolated drive circuit to be used for regulating an output voltage across a load. The isolated drive circuit may charge, discharge, or preserve the load charge using a controller that controls one or more switches. The controller may operate a switch according to an internal/external clock or an external control signal received by the controller. The isolated drive circuit may be an effective solution to simplify the drive design and decrease the amount of energy dissipated by the drive, especially when the load, associated with the drive, requires a high input voltage level.