A Memristor Crossbar Based Computing Engine Optimized for High Speed and Accuracy

Abstract

Matrix-vector multiplication, as a key computing operation, has been largely adopted in applications and hence greatly affects the execution efficiency. A common technique to enhance the performance of matrix-vector multiplication is increasing execution parallelism, which results in higher design cost. In recent years, new devices and structures have been widely investigated as alternative solutions. Among them, memristor crossbar demonstrates a great potential for its intrinsic support of matrix-vector multiplication, high integration density, and built-in parallel execution. However, the computation accuracy and speed of such designs are limited and constrained by the features of crossbar array and peripheral circuitry. In this work, we propose a new memristor crossbar based computing engine design by leveraging a current sensing scheme. High operation parallelism and therefore fast computation can be achieved by simultaneously supplying analog voltages into a memristor crossbar and directly detecting weighted currents through current amplifiers. The performance and effectiveness of the proposed design were examined through the implementation of a neural network for pattern recognition based on MNIST database. Compared to a prior reported design, ours increases the recognition accuracy 8.1% (to 94.6%).