A CMOS-memristor hybrid system for implementing stochastic binary spike timing-dependent plasticity

Abstract

This paper describes a fully experimental hybrid system in which a 4 × 4 memristive crossbar spiking neural network (SNN) was assembled using custom high-resistance state memristors with analogue CMOS neurons fabricated in 180nm CMOS technology. The custom memristors used NMOS selector transistors, made available on a second 180nm CMOS chip. One drawback is that memristors operate with currents in the micro-Amperes range, while analogue CMOS neurons may need to operate with currents in the pico-Amperes range. One possible solution was to use a compact circuit to scale the memristor-domain currents down to the analogue CMOS neuron domain currents by at least 5 6 orders of magnitude. Here, we proposed using an on-chip compact current splitter circuit based on MOS ladders to aggressively attenuate the currents by over 5 orders of magnitude. This circuit was added before each neuron. This paper describes the proper experimental operation of an SNN circuit using a 4 × 4 1T1R synaptic crossbar together with four post-synaptic CMOS circuits, each with a 5-decade current attenuator and an integrateand-fire neuron. It also demonstrates one-shot winnertakes-all training and stochastic binary spike-Timingdependent-plasticity learning using this small system. This article is part of the theme issue 'Advanced neurotechnologies: Translating innovation for health and well-being .