Im, Seongil Hwang, Jingyeong Jeong, Jae-Seung Lee, Hyejin Park, Min Hyuk Cho, Jeong Ho Ju, Hyunsu Lee, Suyoun 2024-07-26T05:00:05Z 2024-07-26T05:00:05Z 2024-07-25 2024-09 0960-0779 https://pubs.kist.re.kr/handle/201004/150294 Recent advancements in artificial intelligence systems have been propelled spectacularly by the progress in machine learning techniques, particularly deep neural networks and spiking neural networks. However, such software and CMOS-based approaches present challenges in terms of energy efficiency and scalability. To address these issues, there has been growing interest in the development of energy-efficient ML techniques centered around the restricted Boltzmann machine (RBM). The RBM capitalizes on the Contrastive Divergence, a local learning rule that reduces computational load and energy consumption. Additionally, the RBM can serve as a foundational unit for the deep belief net (DBN). In this study, a simple stochastic neuron device composed of the Ovonic threshold switch (OTS) connected in series with a resistor (Rload) is proposed. Demonstrating probabilistic switching that follows a sigmoid function, this behavior can be adjusted based on the width and interval of the input pulses. Through simulation studies, the device demonstrated successful application in the recognition and reconstruction of handwritten digits. English Pergamon Press Ltd. Stochastic artificial neuron based on Ovonic Threshold Switch (OTS) and its applications for Restricted Boltzmann Machine (RBM) Article 10.1016/j.chaos.2024.115195 1 Chaos, Solitons & Fractals, v.186 Chaos, Solitons & Fractals 186 Y scie scopus 001267438300001 2-s2.0-85197817545 Mathematics, Interdisciplinary Applications Physics, Multidisciplinary Physics, Mathematical Mathematics Physics Article MODEL Stochastic artificial neuron Neuromorphic computing Restricted Boltzmann Machine (RBM) Deep Belief Network (DBN) Ovonic Threshold Switch (OTS)