A novel RRAM-based adaptive-threshold LIF neuron circuit for high recognition accuracy

Abstract

A novel leaky integrate-and-fire (LIF) neuron circuit based on the gradual switching in resistive random access memory (RRAM) device is put forward, in which threshold modulation can be achieved. Its threshold modulation and spike generating functions are verified through HSPICE simulation. In unsupervised pattern recognition for handwritten digits in MNIST dataset, its advantage in improving the accuracy (from about 70% to more than 95%) is demonstrated. Benchmarking results indicate that this novel neuron is much faster and can save about 66% area compared to one previously proposed.