Highly-scalable stochastic neuron based on Ovonic Threshold Switch (OTS) and its applications in Restricted Boltzmann Machine (RBM)

TL;DR

This paper introduces a scalable stochastic neuron based on Ovonic Threshold Switch (OTS) that can generate Boltzmann-distributed randomness, suitable for hardware RBMs, demonstrated through simulations on MNIST with promising results.

Contribution

The paper presents a novel OTS-based stochastic neuron device that reliably produces Boltzmann-distributed randomness for hardware RBMs, with demonstrated applications in image recognition and denoising.

Findings

Achieved 86.07% accuracy on MNIST digit recognition.

Passed 15 out of 16 NIST statistical tests for randomness.

Successfully demonstrated image denoising using the proposed device.

Abstract

Interest in Restricted Boltzmann Machine (RBM) is growing as a generative stochastic artificial neural network to implement a novel energy-efficient machine-learning (ML) technique. For a hardware implementation of the RBM, an essential building block is a reliable stochastic binary neuron device that generates random spikes following the Boltzmann distribution. Here, we propose a highly-scalable stochastic neuron device based on Ovonic Threshold Switch (OTS) which utilizes the random emission and capture process of traps as the source of stochasticity. The switching probability is well described by the Boltzmann distribution, which can be controlled by operating parameters. As a candidate for a true random number generator (TRNG), it passes 15 among the 16 tests of the National Institute of Standards and Technology (NIST) Statistical Test Suite (Special Publication 800-22). In addition, the recognition task of handwritten digits (MNIST) is demonstrated using a simulated RBM network consisting of the proposed device with a maximum recognition accuracy of 86.07 %. Furthermore, reconstruction of images is successfully demonstrated using images contaminated with noises, resulting in images with the noise removed. These results show the promising properties of OTS-based stochastic neuron devices for applications in RBM systems.