ERROR DETECTION IN CONVOLUTIONAL OPERATIONS
    1.
    发明公开

    公开(公告)号:US20240020419A1

    公开(公告)日:2024-01-18

    申请号:US17812834

    申请日:2022-07-15

    Applicant: Arm Limited

    CPC classification number: G06F21/64 G06F16/2365 G06F16/2264

    Abstract: Methods and systems for detecting errors when performing a convolutional operation is provided. Predicted checksum data, corresponding to input checksum data and kernel checksum data, is obtained. The convolutional operation is performed to obtain an output feature map. Output checksum data is generated and the predicted checksum data and the output checksum data are compared, the comparing taking account of partial predicted checksum data configured to correct for a lack of padding when performing the convolution operation, wherein the partial predicted checksum data corresponds to input checksum data for a subset of the values in the input feature map and kernel checksum data for a subset of the values in the kernel.

    Matrix Multiplication System, Apparatus and Method

    公开(公告)号:US20210124560A1

    公开(公告)日:2021-04-29

    申请号:US16663887

    申请日:2019-10-25

    Applicant: Arm Limited

    Abstract: The present disclosure advantageously provides a system, matrix multiply accelerator (MMA) and method for efficiently multiplying matrices. The MMA includes a vector register to store the row vectors of one input matrix, a vector register to store the column vectors of another input matrix, a vector register to store an output matrix, and an array of vector multiply and accumulate (VMAC) units coupled to the vector registers. Each VMAC unit is coupled to at least two row vector signal lines and at least two column vector signal lines, and is configured to calculate the dot product for one element i,j of the output matrix by multiplying each row vector formed from the ith row of the first matrix with a corresponding column vector formed from the jth column of the second matrix to generate intermediate products, and accumulate the intermediate products into a scalar value.

Patent Agency Ranking