Spiking Neural Network Using Synaptic Transistors and Neuron Circuits for Pattern Recognition With Noisy Images

We demonstrate the hardware implementation of spiking neural network (SNN) with synaptic transistors and neuron circuits. The method of conversion from software fully-connected network (FCN) to hardware SNN with little degradation is discussed. The degradation of classification accuracy is analyzed...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE electron device letters Ročník 39; číslo 4; s. 630 - 633
Hlavní autori: Kim, Hyungjin, Hwang, Sungmin, Park, Jungjin, Yun, Sangdoo, Lee, Jong-Ho, Park, Byung-Gook
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: IEEE 01.04.2018
Predmet:
Biological neural networks
Degradation
Denoising autoencoder (DAE)
Hardware
neuromorphic system
neuron circuit
Neurons
Noise measurement
pattern recognition
Software
spiking neural network (SNN)
synaptic device
Transistors
ISSN:0741-3106, 1558-0563
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:We demonstrate the hardware implementation of spiking neural network (SNN) with synaptic transistors and neuron circuits. The method of conversion from software fully-connected network (FCN) to hardware SNN with little degradation is discussed. The degradation of classification accuracy is analyzed in terms of device variation and noisy images. In addition, the accuracy degradation is significantly improved by stacking denoising autoencoder (DAE) layer. FCN–SNN conversion with very little performance drop is demonstrated using weight normalization, and SNN with DAE layer shows a great tolerance to input image noise.
ISSN:0741-3106
1558-0563
DOI:10.1109/LED.2018.2809661