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Implementing neural nets with programmable logicNetworks of Boolean programmable logic modules are presented as one purely digital class of artificial neural nets. The approach contrasts with the continuous analog framework usually suggested. Programmable logic networks are capable of handling many neural-net applications. They avoid some of the limitations of threshold logic networks and present distinct opportunities. The network nodes are called dynamically programmable logic modules. They can be implemented with digitally controlled demultiplexers. Each node performs a Boolean function of its inputs which can be dynamically assigned. The overall network is therefore a combinational circuit and its outputs are Boolean global functions of the network's input variables. The approach offers definite advantages for VLSI implementation, namely, a regular architecture with limited connectivity, simplicity of the control machinery, natural modularity, and the support of a mature technology.
Document ID
19880058512
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Vidal, Jacques J.
(California, University Los Angeles, United States)
Date Acquired
August 13, 2013
Publication Date
July 1, 1988
Publication Information
Publication: IEEE Transactions on Acoustics, Speech, and Signal Processing
Volume: 36
ISSN: 0096-3518
Subject Category
Computer Systems
Accession Number
88A45739
Funding Number(s)
CONTRACT_GRANT: NAG2-302
Distribution Limits
Public
Copyright
Other

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