Memristive Monte Carlo DropConnect crossbar array enabled by device and algorithm co-design

Abstract

Device and algorithm co-design aims to develop energy-efficient hardware that directly implements complex algorithms and optimizes algorithms to match the hardware's characteristics. Specifically, neuromorphic computing algorithms are constantly growing in complexity, necessitating an ongoing search for hardware implementations capable of handling these intricate algorithms. Here, we present a memristive Monte Carlo DropConnect (MC-DC) crossbar array developed through a hardware algorithm co-design approach. To implement the MC-DC neural network, stochastic switching and analog memory characteristics are required, and we achieved them using Ag-based diffusive selectors and Ru-based electrochemical metalization (ECM) memristors, respectively. The devices were integrated with a one-selector one-memristor (1S1M) structure, and their well-matched operating voltages and currents enabled stochastic readout and deterministic analog programming. With the integrated hardware, we successfully demonstrated the MC-DC operation. Additionally, the selector allowed for the control of switching polarity, and by understanding this hardware characteristic, we were able to modify the algorithm to fit it and further improve the network performance.