Trainable Neuromorphic Spintronic Hardware Via Analog Finite-Difference Gradient Methods

TL;DR

This paper presents a novel hardware architecture for spintronic neural networks that enables on-device gradient generation and training using analog finite-difference methods, achieving high accuracy despite device variability.

Contribution

It introduces an analog finite-difference approach for on-chip training of spintronic neural networks, demonstrated experimentally with high accuracy and scalability potential.

Findings

Magnetic tunnel junctions can produce complex nonlinear responses.

The proposed method achieves 93.3% classification accuracy.

Gradients closely match numerical values without extra computation.

Abstract

Spintronic nano-neurons offer a promising route towards energy-efficient, high-performance hardware neural networks thanks to their inherent low-input nonlinear dynamics. However, training such networks remains a major bottleneck as it depends on oversimplified models of device behaviour and is highly sensitive to device variability. Here, we introduce a hardware architecture that overcomes these limitations by enabling on-device generation of gradients. First, we introduce theoretically and demonstrate experimentally that magnetic tunnel junctions can generate tunable and complex nonlinear responses. Building on this, we implement an analogue finite-difference approach to enable on-chip training in spintronic neural networks with one and two hidden layers. We experimentally implemented device in the loop backpropagation in a magnetic tunnel junction based neural network, achieving a classification accuracy of 93.3% despite pronounced device variability. During training, the gradients generated by the proposed analog neurons closely match the values derived numerically, without incurring computational overhead. Via physical simulations, we also demonstrate that this approach can be scaled up to support training in deep architectures. Our results pave the way for reliable, trainable and fully analogue spintronic neural networks, opening up new possibilities for next-generation, energy-efficient artificial intelligence hardware.