One of the big challenges of current electronics is the design and implementation of hardware neural networks that perform fast and energy-efficient machine learning. Spintronics is a promising catalyst for this field with the capabilities of nanosecond operation and compatibility with existing microelectronics. Considering large-scale, viable neuromorphic systems however, variability of device properties is a serious concern. In this paper, we show an autonomously operating circuit that performs hardware-aware machine learning utilizing probabilistic neurons built with stochastic magnetic tunnel junctions. We show that insitu learning of weights and biases in a Boltzmann machine can counter device-to-device variations and learn the probability distribution of meaningful operations such as a full adder. This scalable autonomously operating learning circuit using spintronics-based neurons could be especially of interest for standalone artificial-intelligence devices capable of fast and efficient learning at the edge.
@article{arxiv.2102.05137,
title = {Hardware-aware $in \ situ$ Boltzmann machine learning using stochastic magnetic tunnel junctions},
author = {Jan Kaiser and William A. Borders and Kerem Y. Camsari and Shunsuke Fukami and Hideo Ohno and Supriyo Datta},
journal= {arXiv preprint arXiv:2102.05137},
year = {2022}
}