Hardware in Loop Learning with Spin Stochastic Neurons

Abstract

Despite the promise of superior efficiency and scalability, real-world deployment of emerging nanoelectronic platforms for brain-inspired computing have been limited thus far, primarily because of inter-device variations and intrinsic non-idealities. In this work, mitigation of these issues is demonstrated by performing learning directly on practical devices through a hardware-in-loop approach, utilizing stochastic neurons based on heavy metal/ferromagnetic spin–orbit torque heterostructures. The probabilistic switching and device-to-device variability of the fabricated devices of various sizes is characterized to showcase the effect of device dimension on the neuronal dynamics and its consequent impact on network-level performance. The efficacy of the hardware-in-loop scheme is illustrated in a deep learning scenario achieving equivalent software performance. This work paves the way for future large-scale implementations of neuromorphic hardware and realization of truly autonomous edge-intelligent devices.