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, we demonstrate mitigating these issues 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. We characterize the probabilistic switching and device-to-device variability of our fabricated devices of various sizes 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.
@article{arxiv.2305.03235,
title = {Hardware in Loop Learning with Spin Stochastic Neurons},
author = {A N M Nafiul Islam and Kezhou Yang and Amit K. Shukla and Pravin Khanal and Bowei Zhou and Wei-Gang Wang and Abhronil Sengupta},
journal= {arXiv preprint arXiv:2305.03235},
year = {2024}
}