High-Speed CMOS-Free Purely Spintronic Asynchronous Recurrent Neural Network

TL;DR

This paper introduces a high-speed, CMOS-free, purely spintronic asynchronous recurrent neural network inspired by biological systems, demonstrating improved efficiency and speed over traditional architectures using emerging spintronic devices.

Contribution

It presents the first fully spintronic neuron integrated into a Hopfield RNN, showcasing a novel approach to neuromorphic computing with enhanced performance.

Findings

The spintronic RNN outperforms CMOS-based architectures in speed.

The proposed design demonstrates improved energy efficiency.

The network successfully solves associative memory tasks.

Abstract

Neuromorphic computing systems overcome the limitations of traditional von Neumann computing architectures. These computing systems can be further improved upon by using emerging technologies that are more efficient than CMOS for neural computation. Recent research has demonstrated memristors and spintronic devices in various neural network designs boost efficiency and speed. This paper presents a biologically inspired fully spintronic neuron used in a fully spintronic Hopfield RNN. The network is used to solve tasks, and the results are compared against those of current Hopfield neuromorphic architectures which use emerging technologies.