A CMOS Spiking Neuron for Brain-Inspired Neural Networks with Resistive Synapses and In-Situ Learning

TL;DR

This paper introduces a compact CMOS spiking neuron capable of in-situ learning and driving large resistive synapse arrays, advancing brain-inspired neuromorphic computing with high efficiency.

Contribution

A novel leaky integrate-and-fire neuron design that integrates dual-mode operation and in-situ learning in a small CMOS chip.

Findings

Can drive 1000 resistive synapses

Demonstrates in-situ associative learning

Achieves 9.3 pJ/spike/synapse energy efficiency

Abstract

Nanoscale resistive memories are expected to fuel dense integration of electronic synapses for large-scale neuromorphic system. To realize such a brain-inspired computing chip, a compact CMOS spiking neuron that performs in-situ learning and computing while driving a large number of resistive synapses is desired. This work presents a novel leaky integrate-and-fire neuron design which implements the dual-mode operation of current integration and synaptic drive, with a single opamp and enables in-situ learning with crossbar resistive synapses. The proposed design was implemented in a 0.18 $\mu$m CMOS technology. Measurements show neuron's ability to drive a thousand resistive synapses, and demonstrate an in-situ associative learning. The neuron circuit occupies a small area of 0.01 mm$^2$ and has an energy-efficiency of 9.3 pJ$/$spike$/$synapse.