Memristor-Based Analog Computation and Neural Network Classification with a Dot Product Engine

Abstract

Using memristor crossbar arrays to accelerate computations is a promising approach to efficiently implement algorithms in deep neural networks. Early demonstrations, however, are limited to simulations or small-scale problems primarily due to materials and device challenges that limit the size of the memristor crossbar arrays that can be reliably programmed to stable and analog values, which is the focus of the current work. High-precision analog tuning and control of memristor cells across a 128 × 64 array is demonstrated, and the resulting vector matrix multiplication (VMM) computing precision is evaluated. Single-layer neural network inference is performed in these arrays, and the performance compared to a digital approach is assessed. Memristor computing system used here reaches a VMM accuracy equivalent of 6 bits, and an 89.9% recognition accuracy is achieved for the 10k MNIST handwritten digit test set. Forecasts show that with integrated (on chip) and scaled memristors, a computational efficiency greater than 100 trillion operations per second per Watt is possible.

Large memristor arrays composed of hafnium oxide are demonstrated with suitability for computing matrix operations at higher power efficiency than digital systems. The nonmemory application of memristors is performed in an analog computing platform. Computational operations with 6 bit equivalent precision are shown and utilized to directly compute neural network inference within a memristor crossbar.

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