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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on computers Jg. 67; H. 9; S. 1273 - 1286
Hauptverfasser:	Yidong Liu, Siting Liu, Yanzhi Wang, Lombardi, Fabrizio, Jie Han
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.09.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!