Domain Wall Motion-based XOR-like Activation Unit With A Programmable Threshold

Suman Deb, Tarun Vatwani, Anupam Chattopadhyay, Arindam Basu, Xuanyao Fong

Abstract: Spintronic devices promise an excellent opportunity for implementing ultra-low power neuromorphic platforms due to the inherent correspondence between their physical characteristics and the required neuronal, synaptic functionalities. Neuromorphic circuits using domain wall motion-based threshold neurons have been demonstrated in previous studies. However, threshold neurons are unable to realize linearly inseparable functions. Our work addresses this challenge by proposing a new domain wall motion-based neural activation unit with XOR-like activation function. We also develop a new learning algorithm for neurons with this activation unit. Offline training is performed on real-world datasets from the UCI machine learning repository. Neuromorphic circuits corresponding to these datasets are also simulated. The results suggest femto-Joule range energy consumption of a neuron with the proposed activation unit and 1.08×-1.82× lower misclassification rate (MCR) of the proposed algorithm in comparison to the traditional perceptron learning algorithm.