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Systems and methods for reducing power consumption of convolution operations for artificial neural networks

机译:降低人工神经网络卷积操作功耗的系统和方法

摘要

A computer-implemented method may include maintaining, within a local memory device (LMD) in a hardware accelerator (1) a filter matrix that may include a set of filter vectors corresponding to a filter location in each of a set of filters of a convolutional layer of an artificial neural network, and (2) an activation matrix that may include a primary and a secondary set of activation vectors, each activation vector included in an activation volume. The method may also include (1) directing a matrix multiplication unit (MMU) in the hardware accelerator to execute a matrix multiplication operation (MMO) using the filter matrix and the activation matrix, (2) replacing (i) the filter matrix with an additional filter matrix, and (ii) the secondary set of activation vectors with an additional set of activation vectors, and (3) directing the MMU to execute an additional MMO using the additional filter matrix and the activation matrix.
机译:计算机实现的方法可以包括在硬件加速器(1)中的局部存储器设备(LMD)内维护滤波器矩阵,该滤波器矩阵可包括与卷积器的一组滤波器中的每一个滤波器中的滤波器位置相对应的一组滤波器矢量 人工神经网络层,和(2)可包括主和次生激活载体的激活矩阵,包括在激活体积中的每个激活载体。 该方法还可以包括(1)在硬件加速器中引导矩阵乘法单元(MMU),以执行使用滤波器矩阵和激活矩阵的矩阵乘法操作(MMO),(2)用滤波矩阵替换(i) 附加滤波器矩阵,和(ii)具有附加的一组激活矢量的次级激活矢量,并且(3)指示MMU使用附加滤波矩阵和激活矩阵执行附加MMO。

著录项

  • 公开/公告号US11120328B1

    专利类型

  • 公开/公告日2021-09-14

    原文格式PDF

  • 申请/专利权人 FACEBOOK INC.;

    申请/专利号US201916354665

  • 发明设计人 KRISHNAKUMAR NARAYANAN NAIR;

    申请日2019-03-15

  • 分类号G06N3/04;G06N3/08;G06N3/10;G06F17/16;

  • 国家 US

  • 入库时间 2022-08-24 21:01:22

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