Pulse-Width Modulation based Dot-Product Engine for Neuromorphic Computing System using Memristor Crossbar Array

The Dot-Product Engine (DPE) is a critical circuit for implementing neural networks in hardware. The recent-developed memristor crossbar array technology, which is able to efficiently carry out dot-product multiplication and update its weights in real time, has been considered as one of the viable technologies to build a high-efficient neural network computing system. In this paper, the Pulse-Width-Modulation (PWM) based DPE has been presented and analyzed. Here, the PWM based signal, instead of the traditional amplitude modulated (AM) signal, is used as the computation variable. Comparing to the existing AM based system, this PWM counterpart provides an alternative approach to reduce the power consumption and chip area of its peripheral circuits. Power and area saving becomes more prominent when the size and/or the number of arrays increase. This new approach also provides the critically needed scalability to accommodate the computation variable with higher precision. In this paper, a 4-bit (can be easily expanded to 8-bit) feed forward neural network with 3-bit weights (memristor's conductance) is constructed using the proposed PWM DPE to identify digits from the MNIST data set. The circuit system is implemented in 130 nm standard CMOS technology. The entire circuit system consumes about 53mW with more than 86% recognition accuracy in average.