O(n) depth-3 binary addition

We investigate small depth and size feed-forward neural networks performing binary addition. We propose a set of equations that can be used to realize small depth inexpensive networks for arbitrary operand lengths. In particular we show that O(n) depth-3 networks for the binary addition can be easil...

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Bibliographic Details
Published inProceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers Vol. 1; pp. 536 - 539 vol.1
Main Authors Vassiliadis, S., Bertels, K.
Format Conference Proceeding
LanguageEnglish
Published IEEE Comput. Soc. Press 1994
Subjects
Area measurement
Artificial neural networks
Boolean functions
Computer networks
Equations
Feedforward neural networks
Feedforward systems
Neural networks
Neurons
Velocity measurement
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Summary:We investigate small depth and size feed-forward neural networks performing binary addition. We propose a set of equations that can be used to realize small depth inexpensive networks for arbitrary operand lengths. In particular we show that O(n) depth-3 networks for the binary addition can be easily constructed having small weight sizes. We also describe a scheme for the design of 32-bit binary adders. When compared to the addition scheme known to produce the least expensive adders on terms of area, using feed-forward neural networks, our scheme requires only 20% of the area in terms of neurons. Consequently our design provides substantial area reduction.< >
ISBN:0818664053
9780818664052
ISSN:1058-6393
2576-2303
DOI:10.1109/ACSSC.1994.471510