Abstract: Apparatuses, systems, and techniques to determine a matrix multiplication algorithm for a matrix multiplication operation. In at least one embodiment, a matrix multiplication operation is analyzed to determine an appropriate matrix multiplication algorithm to perform the matrix multiplication algorithm.

Abstract: An apparatus of an aspect includes a plurality of cores and shared core extension logic coupled with each of the plurality of cores. The shared core extension logic has shared data processing logic that is shared by each of the plurality of cores. Instruction execution logic, for each of the cores, in response to a shared core extension call instruction, is to call the shared core extension logic. The call is to have data processing performed by the shared data processing logic on behalf of a corresponding core. Other apparatus, methods, and systems are also disclosed.

Abstract: An apparatus of an aspect includes a plurality of cores and shared core extension logic coupled with each of the plurality of cores. The shared core extension logic has shared data processing logic that is shared by each of the plurality of cores. Instruction execution logic, for each of the cores, in response to a shared core extension call instruction, is to call the shared core extension logic. The call is to have data processing performed by the shared data processing logic on behalf of a corresponding core. Other apparatus, methods, and systems are also disclosed.

Abstract: Apparatuses, systems, and techniques to determine a matrix multiplication algorithm for a matrix multiplication operation. In at least one embodiment, a matrix multiplication operation is analyzed to determine an appropriate matrix multiplication algorithm to perform the matrix multiplication algorithm.

Abstract: Apparatuses, systems, and techniques to improve neural network computations. In at least one embodiment, a deep neural network library receives computation descriptors from one or more users and generates an optimized execution plan comprising one or more optimized operations to facilitate neural network computing.

Abstract: An apparatus of an aspect includes a plurality of cores and shared core extension logic coupled with each of the plurality of cores. The shared core extension logic has shared data processing logic that is shared by each of the plurality of cores. Instruction execution logic, for each of the cores, in response to a shared core extension call instruction, is to call the shared core extension logic. The call is to have data processing performed by the shared data processing logic on behalf of a corresponding core. Other apparatus, methods, and systems are also disclosed.

Abstract: Apparatuses, systems, and techniques to determine a matrix multiplication algorithm for a matrix multiplication operation. In at least one embodiment, a matrix multiplication operation is analyzed to determine an appropriate matrix multiplication algorithm to perform the matrix multiplication algorithm.

Abstract: Apparatuses, systems, and techniques to determine a matrix multiplication algorithm for a matrix multiplication operation. In at least one embodiment, a matrix multiplication operation is analyzed to determine an appropriate matrix multiplication algorithm to perform the matrix multiplication algorithm.

Abstract: Apparatuses, systems, and techniques are presented to compile code. In at least one embodiment, one or more compilers are to compile one or more compiled portions of code with one or more intermediate representations of one or more portions of code.

Abstract: An apparatus of an aspect includes a plurality of cores and shared core extension logic coupled with each of the plurality of cores. The shared core extension logic has shared data processing logic that is shared by each of the plurality of cores. Instruction execution logic, for each of the cores, in response to a shared core extension call instruction, is to call the shared core extension logic. The call is to have data processing performed by the shared data processing logic on behalf of a corresponding core. Other apparatus, methods, and systems are also disclosed.

Abstract: An apparatus of an aspect includes a plurality of cores and shared core extension logic coupled with each of the plurality of cores. The shared core extension logic has shared data processing logic that is shared by each of the plurality of cores. Instruction execution logic, for each of the cores, in response to a shared core extension call instruction, is to call the shared core extension logic. The call is to have data processing performed by the shared data processing logic on behalf of a corresponding core. Other apparatus, methods, and systems are also disclosed.

Abstract: An application binary interface (ABI) can be exposed in a processor to enable blocks of threads, which may correspond to separately compiled operators, to communicate without storing data to global memory external to the processor. The ABI can define how results of one computation, corresponding to a first thread block, will be organized in registers and shared memory of a processor at the end of one operator (i.e., kernel). The start of the next operator (i.e., kernel), corresponding to a second thread block, can consume the results from the registers and shared memory. Data can be stored to processor local storage for individual threads as they exit the block. Once published, libraries can be separately compiled, optimized, and tested as long as they adhere to the published ABI.

Abstract: Embodiments of systems, apparatuses, and methods for performing in a computer processor vector double block packed sum of absolute differences (SAD) in response to a single vector double block packed sum of absolute differences instruction that includes a destination vector register operand, first and second source operands, an immediate, and an opcode are described.

Abstract: An apparatus of an aspect includes a plurality of cores and shared core extension logic coupled with each of the plurality of cores. The shared core extension logic has shared data processing logic that is shared by each of the plurality of cores. Instruction execution logic, for each of the cores, in response to a shared core extension call instruction, is to call the shared core extension logic. The call is to have data processing performed by the shared data processing logic on behalf of a corresponding core. Other apparatus, methods, and systems are also disclosed.

Abstract: An apparatus of an aspect includes a plurality of cores and shared core extension logic coupled with each of the plurality of cores. The shared core extension logic has shared data processing logic that is shared by each of the plurality of cores. Instruction execution logic, for each of the cores, in response to a shared core extension call instruction, is to call the shared core extension logic. The call is to have data processing performed by the shared data processing logic on behalf of a corresponding core. Other apparatus, methods, and systems are also disclosed.

Abstract: An apparatus of an aspect includes a plurality of cores and shared core extension logic coupled with each of the plurality of cores. The shared core extension logic has shared data processing logic that is shared by each of the plurality of cores. Instruction execution logic, for each of the cores, in response to a shared core extension call instruction, is to call the shared core extension logic. The call is to have data processing performed by the shared data processing logic on behalf of a corresponding core. Other apparatus, methods, and systems are also disclosed.

Abstract: Flood-fill techniques and architecture are disclosed. In accordance with one embodiment, the architecture comprises a hardware primitive with a software interface which collectively allow for both data-based and task-based parallelism in executing a flood-fill process. The hardware primitive is defined to do the flood-fill function and is scalable and may be implemented with a bitwise definition that can be tuned to meet power/performance targets, in some embodiments. In executing a flood-fill operation, and in accordance with an example embodiment, the software interface produces parallel threads and issues them to processing elements, such that each of the threads can run independently until done. Each processing element in turn accesses a flood-fill hardware primitive, each of which is configured to flood a seed inside an N×M image block. In some cases, processing element commands to the flood-fill hardware primitive(s) can be queued and acted upon pursuant to an arbitration scheme.

Abstract: Embodiments of systems, apparatuses, and methods for performing in a computer processor vector double block packed sum of absolute differences (SAD) in response to a single vector double block packed sum of absolute differences instruction that includes a destination vector register operand, first and second source operands, an immediate, and an opcode are described.

Abstract: An apparatus of an aspect includes a plurality of cores and shared core extension logic coupled with each of the plurality of cores. The shared core extension logic has shared data processing logic that is shared by each of the plurality of cores. Instruction execution logic, for each of the cores, in response to a shared core extension call instruction, is to call the shared core extension logic. The call is to have data processing performed by the shared data processing logic on behalf of a corresponding core. Other apparatus, methods, and systems are also disclosed.

Abstract: Embodiments of systems, apparatuses, and methods for performing in a computer processor vector packed horizontal add or subtract of packed data elements in response to a single vector packed horizontal add or subtract instruction that includes a destination vector register operand, a source vector register operand, and an opcode are describes.