NDLI: Multiplierless multilayer feedforward neural networks

Content Provider	IEEE Xplore Digital Library
Author	Kwan, H.K. Tang, C.Z.
Copyright Year	1993
Description	Author affiliation: Dept. of Electr. Eng., Windsor Univ., Ont., Canada (Kwan, H.K.; Tang, C.Z.)
Abstract	A design algorithm for multiplierless 2-layer feedforward neural networks suitable for discrete input-output mapping is proposed in this paper. By using this algorithm, the obtained network has continuous valued weights at the first layer and single-term powers-of-two valued weights at the second layer such that no multiplications are needed in the computation after training. On the other hand, step function is used at the output layer and a simplified version of sigmoid function is used at the hidden layer as activation functions which simplifies digital hardware implementation further. Simulation results showed that such networks can retain nearly identical recall performance of the corresponding networks using continuous weights, while having increased computational speed in applications and reduced cost in digital hardware implementation.<>
Starting Page	1085
Ending Page	1088
File Size	260143
Page Count	4
File Format	PDF
ISBN	0780317602
DOI	10.1109/MWSCAS.1993.343273
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1993-08-16
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multi-layer neural network Neural networks Feedforward neural networks Neurons Algorithm design and analysis Computer networks Hardware Artificial neural networks Computational modeling Computer applications
Content Type	Text
Resource Type	Article

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