Domain Wall Leaky Integrate-and-Fire Neurons with Shape-Based Configurable Activation Functions

TL;DR

This paper introduces a novel spintronic neuron design with configurable activation functions, enhancing neuromorphic computing by eliminating the need for CMOS components and enabling diverse activation shapes.

Contribution

It proposes a new spintronic neuron architecture that allows shape-based activation function configuration, including linear and sigmoidal types, improving flexibility and efficiency in neuromorphic systems.

Findings

Demonstrated linear and sigmoidal activation functions in spintronic neurons

Achieved shape-based configurability of activation functions

Reduced reliance on CMOS components in neuromorphic devices

Abstract

Complementary metal oxide semiconductor (CMOS) devices display volatile characteristics, and are not well suited for analog applications such as neuromorphic computing. Spintronic devices, on the other hand, exhibit both non-volatile and analog features, which are well-suited to neuromorphic computing. Consequently, these novel devices are at the forefront of beyond-CMOS artificial intelligence applications. However, a large quantity of these artificial neuromorphic devices still require the use of CMOS, which decreases the efficiency of the system. To resolve this, we have previously proposed a number of artificial neurons and synapses that do not require CMOS for operation. Although these devices are a significant improvement over previous renditions, their ability to enable neural network learning and recognition is limited by their intrinsic activation functions. This work proposes modifications to these spintronic neurons that enable configuration of the activation functions through control of the shape of a magnetic domain wall track. Linear and sigmoidal activation functions are demonstrated in this work, which can be extended through a similar approach to enable a wide variety of activation functions.