A Hardware Friendly Unsupervised Memristive Neural Network with Weight Sharing Mechanism

TL;DR

This paper presents a hardware-friendly memristive neural network that uses digital circuits for memristor functions, enabling efficient large-scale unsupervised learning with resource sharing and high accuracy.

Contribution

It introduces a digital circuit-based memristive neural network with a novel weight sharing mechanism for scalable, resource-efficient neuromorphic computing.

Findings

Significant hardware resource savings achieved.

Maintains high recognition accuracy.

Resource increase grows slower than network scale.

Abstract

Memristive neural networks (MNNs), which use memristors as neurons or synapses, have become a hot research topic recently. However, most memristors are not compatible with mainstream integrated circuit technology and their stabilities in large-scale are not very well so far. In this paper, a hardware friendly MNN circuit is introduced, in which the memristive characteristics are implemented by digital integrated circuit. Through this method, spike timing dependent plasticity (STDP) and unsupervised learning are realized. A weight sharing mechanism is proposed to bridge the gap of network scale and hardware resource. Experiment results show the hardware resource is significantly saved with it, maintaining good recognition accuracy and high speed. Moreover, the tendency of resource increase is slower than the expansion of network scale, which infers our method's potential on large scale neuromorphic network's realization.