Restricted Boltzmann Machines Implemented by Spin–Orbit Torque Magnetic Tunnel Junctions

Artificial intelligence has surged forward with the advent
of generative
models, which rely heavily on stochastic computing architectures enhanced
by true random number generators with adjustable sampling probabilities.
In this study, we develop spin–orbit torque magnetic tunnel
junctions (SOT-MTJs), investigating their sigmoid-style switching
probability as a function of the driving voltage. This feature proves
to be ideally suited for stochastic computing algorithms such as
the restricted Boltzmann machines (RBM) prevalent in pretraining processes.
We exploit SOT-MTJs as both stochastic samplers and network nodes
for RBMs, enabling the implementation of RBM-based neural networks
to achieve recognition tasks for both handwritten and spoken digits.
Moreover, we further harness the weights derived from the preceding
image and speech training processes to facilitate cross-modal learning
from speech to image generation. Our results clearly demonstrate that
these SOT-MTJs are promising candidates for the development of hardware
accelerators tailored for Boltzmann neural networks and other stochastic
computing architectures.