On-Chip Trainable Spiking Neural Networks Using Time-To-First-Spike Encoding

On-Chip Trainable Spiking Neural Networks Using Time-To-First-Spike Encoding

Artificial Neural Networks (ANNs) have shown remarkable performance in various fields. However, ANN relies on the von-Neumann architecture, which consumes a lot of power. Hardware-based spiking neural networks (SNNs) inspired by a human brain have become an alternative with significantly low power c...

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Bibliographic Details
Main Authors: Jiseong Im, Jaehyeon Kim, Ho-Nam Yoo, Jong-Won Baek, Dongseok Kwon, Seongbin Oh, Jangsaeng Kim, Joon Hwang, Byung-Gook Park, Jong-Ho Lee
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Spiking neural networks (SNNs)
time-to-first-spike (TTFS)
on-chip training
synaptic devices
NAND flash
Online Access:https://ieeexplore.ieee.org/document/9737122/

Internet

https://ieeexplore.ieee.org/document/9737122/

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