A Stochastic Computational Multi-Layer Perceptron with Backward Propagation

Stochastic computation has recently been proposed for implementing artificial neural networks with reduced hardware and power consumption, but at a decreased accuracy and processing speed. Most existing implementations are based on pre-training such that the weights are predetermined for neurons at...

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Vydané v:	IEEE transactions on computers Ročník 67; číslo 9; s. 1273 - 1286
Hlavní autori:	Yidong Liu, Siting Liu, Yanzhi Wang, Lombardi, Fabrizio, Jie Han
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.09.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Accuracy Activation Artificial neural networks Backpropagation binary search Biological neural networks Computation Computer simulation Energy consumption Fixed point arithmetic Floating point arithmetic Hardware Mathematical analysis multi-layer perceptron Multilayer perceptrons neural network Neural networks Neurons Power consumption probability estimator Propagation Rectifiers Redundancy Search algorithms Stochastic computation Training Tunneling magnetoresistance Variation
ISSN:	0018-9340, 1557-9956
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