Abstract: Disclosed embodiments relate to an accelerator for sparse-dense matrix instructions. In one example, a processor to execute a sparse-dense matrix multiplication instruction, includes fetch circuitry to fetch the sparse-dense matrix multiplication instruction having fields to specify an opcode, a dense output matrix, a dense source matrix, and a sparse source matrix having a sparsity of non-zero elements, the sparsity being less than one, decode circuitry to decode the fetched sparse-dense matrix multiplication instruction, execution circuitry to execute the decoded sparse-dense matrix multiplication instruction to, for each non-zero element at row M and column K of the specified sparse source matrix generate a product of the non-zero element and each corresponding dense element at row K and column N of the specified dense source matrix, and generate an accumulated sum of each generated product and a previous value of a corresponding output element at row M and column N of the specified dense output matrix.

Abstract: Disclosed embodiments relate to an accelerator for sparse-dense matrix instructions. In one example, a processor to execute a sparse-dense matrix multiplication instruction, includes fetch circuitry to fetch the sparse-dense matrix multiplication instruction having fields to specify an opcode, a dense output matrix, a dense source matrix, and a sparse source matrix having a sparsity of non-zero elements, the sparsity being less than one, decode circuitry to decode the fetched sparse-dense matrix multiplication instruction, execution circuitry to execute the decoded sparse-dense matrix multiplication instruction to, for each non-zero element at row M and column K of the specified sparse source matrix generate a product of the non-zero element and each corresponding dense element at row K and column N of the specified dense source matrix, and generate an accumulated sum of each generated product and a previous value of a corresponding output element at row M and column N of the specified dense output matrix.

Abstract: Disclosed embodiments relate to an accelerator for sparse-dense matrix instructions. In one example, a processor to execute a sparse-dense matrix multiplication instruction, includes fetch circuitry to fetch the sparse-dense matrix multiplication instruction having fields to specify an opcode, a dense output matrix, a dense source matrix, and a sparse source matrix having a sparsity of non-zero elements, the sparsity being less than one, decode circuitry to decode the fetched sparse-dense matrix multiplication instruction, execution circuitry to execute the decoded sparse-dense matrix multiplication instruction to, for each non-zero element at row M and column K of the specified sparse source matrix generate a product of the non-zero element and each corresponding dense element at row K and column N of the specified dense source matrix, and generate an accumulated sum of each generated product and a previous value of a corresponding output element at row M and column N of the specified dense output matrix.

Abstract: The present disclosure is directed to systems and methods for decomposing systolic array circuitry to provide a plurality of N×N systolic sub-array circuits, apportioning a first tensor or array into a plurality of N×M first input arrays, and apportioning a second tensor or array into a plurality of M×N second input arrays. Systolic array control circuitry transfers corresponding ones of the first input arrays and second input arrays to a respective one of the plurality of N×N systolic sub-array circuits. As the elements included in the first input array and the elements included in the second input array are transferred to the systolic sub-array, the systolic sub-array performs one or more mathematical operations using the first and the second input arrays. The systems and methods beneficially improve the usage of the systolic array circuitry thereby advantageously reducing the number of clock cycles needed to perform a given number of calculations.

Abstract: Disclosed embodiments relate to an accelerator for sparse-dense matrix instructions. In one example, a processor to execute a sparse-dense matrix multiplication instruction, includes fetch circuitry to fetch the sparse-dense matrix multiplication instruction having fields to specify an opcode, a dense output matrix, a dense source matrix, and a sparse source matrix having a sparsity of non-zero elements, the sparsity being less than one, decode circuitry to decode the fetched sparse-dense matrix multiplication instruction, execution circuitry to execute the decoded sparse-dense matrix multiplication instruction to, for each non-zero element at row M and column K of the specified sparse source matrix generate a product of the non-zero element and each corresponding dense element at row K and column N of the specified dense source matrix, and generate an accumulated sum of each generated product and a previous value of a corresponding output element at row M and column N of the specified dense output matrix.

Abstract: Disclosed embodiments relate to an accelerator for sparse-dense matrix instructions. In one example, a processor to execute a sparse-dense matrix multiplication instruction, includes fetch circuitry to fetch the sparse-dense matrix multiplication instruction having fields to specify an opcode, a dense output matrix, a dense source matrix, and a sparse source matrix having a sparsity of non-zero elements, the sparsity being less than one, decode circuitry to decode the fetched sparse-dense matrix multiplication instruction, execution circuitry to execute the decoded sparse-dense matrix multiplication instruction to, for each non-zero element at row M and column K of the specified sparse source matrix generate a product of the non-zero element and each corresponding dense element at row K and column N of the specified dense source matrix, and generate an accumulated sum of each generated product and a previous value of a corresponding output element at row M and column N of the specified dense output matrix.

Abstract: Disclosed embodiments relate to an accelerator for sparse-dense matrix instructions. In one example, a processor to execute a sparse-dense matrix multiplication instruction, includes fetch circuitry to fetch the sparse-dense matrix multiplication instruction having fields to specify an opcode, a dense output matrix, a dense source matrix, and a sparse source matrix having a sparsity of non-zero elements, the sparsity being less than one, decode circuitry to decode the fetched sparse-dense matrix multiplication instruction, execution circuitry to execute the decoded sparse-dense matrix multiplication instruction to, for each non-zero element at row M and column K of the specified sparse source matrix generate a product of the non-zero element and each corresponding dense element at row K and column N of the specified dense source matrix, and generate an accumulated sum of each generated product and a previous value of a corresponding output element at row M and column N of the specified dense output matrix.

Abstract: The present disclosure is directed to systems and methods for decomposing systolic array circuitry to provide a plurality of N×N systolic sub-array circuits, apportioning a first tensor or array into a plurality of N×M first input arrays, and apportioning a second tensor or array into a plurality of M×N second input arrays. Systolic array control circuitry transfers corresponding ones of the first input arrays and second input arrays to a respective one of the plurality of N×N systolic sub-array circuits. As the elements included in the first input array and the elements included in the second input array are transferred to the systolic sub-array, the systolic sub-array performs one or more mathematical operations using the first and the second input arrays. The systems and methods beneficially improve the usage of the systolic array circuitry thereby advantageously reducing the number of clock cycles needed to perform a given number of calculations.

Abstract: Disclosed embodiments relate to an accelerator for sparse-dense matrix instructions. In one example, a processor to execute a sparse-dense matrix multiplication instruction, includes fetch circuitry to fetch the sparse-dense matrix multiplication instruction having fields to specify an opcode, a dense output matrix, a dense source matrix, and a sparse source matrix having a sparsity of non-zero elements, the sparsity being less than one, decode circuitry to decode the fetched sparse-dense matrix multiplication instruction, execution circuitry to execute the decoded sparse-dense matrix multiplication instruction to, for each non-zero element at row M and column K of the specified sparse source matrix generate a product of the non-zero element and each corresponding dense element at row K and column N of the specified dense source matrix, and generate an accumulated sum of each generated product and a previous value of a corresponding output element at row M and column N of the specified dense output matrix.

Abstract: Disclosed embodiments relate to an accelerator for sparse-dense matrix instructions. In one example, a processor to execute a sparse-dense matrix multiplication instruction, includes fetch circuitry to fetch the sparse-dense matrix multiplication instruction having fields to specify an opcode, a dense output matrix, a dense source matrix, and a sparse source matrix having a sparsity of non-zero elements, the sparsity being less than one, decode circuitry to decode the fetched sparse-dense matrix multiplication instruction, execution circuitry to execute the decoded sparse-dense matrix multiplication instruction to, for each non-zero element at row M and column K of the specified sparse source matrix generate a product of the non-zero element and each corresponding dense element at row K and column N of the specified dense source matrix, and generate an accumulated sum of each generated product and a previous value of a corresponding output element at row M and column N of the specified dense output matrix.

Abstract: Disclosed embodiments relate to an accelerator for sparse-dense matrix instructions. In one example, a processor to execute a sparse-dense matrix multiplication instruction, includes fetch circuitry to fetch the sparse-dense matrix multiplication instruction having fields to specify an opcode, a dense output matrix, a dense source matrix, and a sparse source matrix having a sparsity of non-zero elements, the sparsity being less than one, decode circuitry to decode the fetched sparse-dense matrix multiplication instruction, execution circuitry to execute the decoded sparse-dense matrix multiplication instruction to, for each non-zero element at row M and column K of the specified sparse source matrix generate a product of the non-zero element and each corresponding dense element at row K and column N of the specified dense source matrix, and generate an accumulated sum of each generated product and a previous value of a corresponding output element at row M and column N of the specified dense output matrix.

Abstract: A fixed-point multiplier providing reduced energy usage dynamically truncates received operands according to the location of computationally important bits in the operands and provides the truncated operands to a reduced width multiplier offering reduced energy usage. Information about the location of the dynamic truncation is used to properly shift the result of the multiplier to provide an approximation of full multiplication of the operands.

Abstract: A fixed-point multiplier providing reduced energy usage dynamically truncates received operands according to the location of computationally important bits in the operands and provides the truncated operands to a reduced width multiplier offering reduced energy usage. Information about the location of the dynamic truncation is used to properly shift the result of the multiplier to provide an approximation of full multiplication of the operands.