NDLI: Incremental communication for multilayer neural networks

Content Provider	IEEE Xplore Digital Library
Author	Ghorbani, A.A. Bhavsar, V.C.
Copyright Year	1990
Abstract	A new method of inter-neuron communication called incremental communication is presented. In the incremental communication method, instead of communicating the whole value of a variable, only the increment or decrement of its previous value is sent on a communication link. The incremental value may be either a fixed-point or a floating-point value. Multilayer feedforward network architecture is used to illustrate the effectiveness of the proposed communication scheme. The method is applied to three different learning problems and the effect of the precision of incremental input-output values of the neurons on the convergence behavior is examined. It is shown through simulation that for some problems even four-bit precision in fixed- and/or floating-point representations is sufficient for the network to converge. With 8-12 bit precisions almost the same results are obtained as that with the conventional communication using 32-bit precision. The proposed method of communication can lead to significant savings in the intercommunication cost for implementations of artificial neural networks on parallel computers as well as the interconnection cost of direct hardware realizations. The method can be incorporated into most of the current learning algorithms in which inter-neuron communications are required. Moreover, it can be used along with the other limited precision strategies for representation of variables suggested in literature.<>
Sponsorship	IEEE Computational Intelligence Society
Starting Page	1375
Ending Page	1385
Page Count	11
File Size	1062884
File Format	PDF
ISSN	10459227
Volume Number	6
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-11-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multi-layer neural network Neural networks Costs Nonhomogeneous media Neurons Convergence Artificial neural networks Computer networks Concurrent computing Neural network hardware
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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