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

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 to implement various neuromorphic functionalities, 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.

Automated summary

CMOS devices are not suitable for analog applications, while spintronic devices are well suited. However, a large number of spintronic devices still require the use of CMOS, which decreases system efficiency. This study improves the activation functions of spintronic devices to enable better neural network learning and recognition.