ELIMINATING MEMORY BOTTLENECKS FOR DEPTHWISE CONVOLUTIONS

    公开(公告)号:US20230086802A1

    公开(公告)日:2023-03-23

    申请号:US17478609

    申请日:2021-09-17

    Abstract: Certain aspects of the present disclosure provide techniques for efficient depthwise convolution. A convolution is performed with a compute-in-memory (CIM) array to generate CIM output, and at least a portion of the CIM output corresponding to a first output data channel, of a plurality of output data channels in the CIM output, is written to a digital multiply-accumulate (DMAC) activation buffer. A patch of the CIM output is read from the DMAC activation buffer, and weight data is read from a DMAC weight buffer. Multiply-accumulate (MAC) operations are performed with the patch of CIM output and the weight data to generate a DMAC output.

    POWER-EFFICIENT COMPUTE-IN-MEMORY POOLING

    公开(公告)号:US20220012580A1

    公开(公告)日:2022-01-13

    申请号:US16922953

    申请日:2020-07-07

    Inventor: Ankit SRIVASTAVA

    Abstract: A multiply-and-accumulate (MAC) circuit having a plurality of compute-in-memory bitcells is configured to multiply a plurality of stored weight bits with a plurality of input bits to provide a MAC output voltage. A successive approximation analog-to-digital converter includes a capacitive-digital-to-analog-converter (CDAC) configured to subtract a bias voltage from the MAC output voltage to provide a CDAC output voltage. A comparator compares the CDAC output voltage to a fixed reference voltage.

    SPARSITY-AWARE COMPUTE-IN-MEMORY
    9.
    发明申请

    公开(公告)号:US20250124354A1

    公开(公告)日:2025-04-17

    申请号:US18989865

    申请日:2024-12-20

    Abstract: Certain aspects of the present disclosure provide techniques for performing machine learning computations in a compute in memory (CIM) array comprising a plurality of bit cells, including: determining that a sparsity of input data to a machine learning model exceeds an input data sparsity threshold; disabling one or more bit cells in the CIM array based on the sparsity of the input data prior to processing the input data; processing the input data with bit cells not disabled in the CIM array to generate an output value; applying a compensation to the output value based on the sparsity to generate a compensated output value; and outputting the compensated output value.

    ACTIVATION BUFFER ARCHITECTURE FOR DATA-REUSE IN A NEURAL NETWORK ACCELERATOR

    公开(公告)号:US20240256827A1

    公开(公告)日:2024-08-01

    申请号:US18565414

    申请日:2021-07-27

    CPC classification number: G06N3/04

    Abstract: Certain aspects provide an apparatus for signal processing in a neural network. The apparatus generally includes computation circuitry configured to perform a convolution operation, the computation circuitry having multiple input rows, and an activation buffer having multiple buffer segments coupled to the multiple the multiple input rows of the computation circuitry, respectively. In some aspects, each of the multiple buffer segments comprises a first multiplexer having a plurality of multiplexer inputs, and each of the plurality of multiplexer inputs of one of the first multiplexers on one of the multiple buffer segments is coupled to a data output of the activation buffer on another one of the multiple buffer segments.

Patent Agency Ranking