In‐Memory Binary Vector–Matrix Multiplication Based on Complementary Resistive Switches

Abstract

This work studies a computation in‐memory concept for binary multiply‐accumulate operations based on complementary resistive switches (CRS). By exploiting the in‐memory boolean exclusive OR (XOR) operation of single CRS devices, the Hamming Distance (HD) can be calculated if the center electrodes of multiple CRS cells are connected. This HD is linearly encoded in the voltage drop of the common electrode, and from it the result of a binary multiply‐accumulate operation can be calculated. A small‐scale demonstration is experimentally realized and the feasibility of the in‐memory computation concept is confirmed. A simulation study identifies the low resistance state (LRS) variability as the main reason for the variations in the output voltage. The application as a potential hardware accelerator for the inference step of binary neural networks is investigated. Therefore, a 1‐layer fully connected neural network is trained on a binarized version of the MNIST data set and the inference step of the test data set is simulated. The concept achieves a prediction accuracy of approximately 86%.

Keywords

• binary neural networks
• complementary resistive switches
• computation in-memory
• neuromorphic computing
• vector–matrix multiplication