HARDWARE IMPLEMENTATION OF WINDOWED OPERATIONS IN THREE OR MORE DIMENSIONS

    公开(公告)号:US20250148264A1

    公开(公告)日:2025-05-08

    申请号:US19018414

    申请日:2025-01-13

    Abstract: A windowed operation is implemented in at least three traversed dimensions. The windowed operation applies a window having at least three dimensions to data having at least three traversed dimensions, with shifts of the window in all three traversed dimensions. Two dimensions of the at least three traversed dimensions are selected, and the windowed operation is mapped to a plurality of constituent 2-D windowed operations in the selected two dimensions, the 2-D windowed operations applying a slice of the window to a slice of the data, with shifts of the slice of the window in only two dimensions. Each of the plurality of 2-D windowed operations is implemented by at least one hardware accelerator, each 2-D windowed operation producing a respective partial result, and the partial results are assembled to produce the result of the windowed operation.

    SYSTEM AND METHOD OF PERFORMING CONVOLUTION EFFICIENTLY ADAPTING WINOGRAD ALGORITHM

    公开(公告)号:US20240346108A1

    公开(公告)日:2024-10-17

    申请号:US18613443

    申请日:2024-03-22

    CPC classification number: G06F17/153

    Abstract: Systems and methods of performing convolution efficiently adapting the Winograd algorithm are provided. Methods of convolving an input tensor with weights w use hardware comprising a plurality of linear operation engines as part of performing adaptations of a Winograd algorithm, the Winograd algorithm splitting each input channel i of a total of Cin input channels into one or more tiles di and calculating a result A[Σi=1Cin(GwjiGT)∘(BTdiB)]AT for each output channel j, wherein G, B and A are constant matrices. The methods comprise determining a first filter F1 from matrix B wherein the filter F1 comprises n kernels, each kernel being an outer product of two columns of the matrix B; and using the linear operation engines to perform a convolution of the input tensor with the first filter F1.

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