JSSC 2019第1期MemorySRAMNeural Network Accelerator

CONV-SRAM- An Energy-Efficient SRAM With In-Memory Dot-Product Computation for L

提出一种能效优化的SRAM，支持内存内点积计算，用于二进制权重卷积神经网络。

>98%准确率（MNIST数据集测试），6位输入/输出

SRAM内存计算点积运算卷积神经网络能效优化

▸10T位单元SRAM阵列存储1位滤波器权重

▸通过位线电压加权平均实现点积计算

▸局部集成模数转换器计算数字卷积输出

Abstract

This paper presents an energy-efﬁcient static ran- dom access memory (SRAM) with embedded dot-product com- putation capability, for binary-weight convolutional neural net- works. A 10T bit-cell-based SRAM array is used to store the 1-b ﬁlter weights. The array implements dot-product as a weighted average of the bitline voltages, which are propor- tional to the digital input values. Local integrating analog- to-digital converters compute the digital convolution outputs, corresponding to each ﬁlte