Pattern Training, Inference, and Regeneration Demonstration Using On‐Chip Trainable Neuromorphic Chips for Spiking Restricted Boltzmann Machine

Pattern Training, Inference, and Regeneration Demonstration Using On‐Chip Trainable Neuromorphic Chips for Spiking Restricted Boltzmann Machine

A fully silicon‐integrated restricted Boltzmann machine (RBM) with an event‐driven contrastive divergence (eCD) training algorithm is implemented using novel stochastic leaky integrate‐and‐fire (LIF) neuron circuits and six‐transistor/2‐PCM‐resistor (6T2R) synaptic unit cells on 90 nm CMOS technolog...

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Bibliographic Details
Main Authors: Uicheol Shin, Masatoshi Ishii, Atsuya Okazaki, Megumi Ito, Malte J. Rasch, Wanki Kim, Akiyo Nomura, Wonseok Choi, Dooyong Koh, Kohji Hosokawa, Matthew BrightSky, Seiji Munetoh, SangBum Kim
Format: Article
Language:English
Published: Wiley 2022-08-01
Series:Advanced Intelligent Systems
Subjects:
image classifications
neuromorphic computing
pattern regeneration
phase-change memory
spiking neural networks
restricted Boltzmann machines
Online Access:https://doi.org/10.1002/aisy.202200034

Internet

https://doi.org/10.1002/aisy.202200034

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