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Fast arithmetic computing with neural networks

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2 Author(s)
K. -Y. Siu ; Inf. Syst. Lab., Stanford Univ., CA, USA ; J. Bruck

The authors introduce a restricted model of a neuron which is more practical as a model of computation then the classical model of a neuron. The authors define a model of neural networks as a feedforward network of such neurons. Whereas any logic circuit of polynomial size (in n) that computes the product of two n-bit numbers requires unbounded delay, such computations can be done in a neural network with constant delay. The authors improve some known results by showing that the product of two n-bit numbers and sorting of n n-bit numbers can both be computed by a polynomial size neural network using only four unit delays, independent of n . Moreover, the weights of each threshold element in the neural networks require only O(log n)-bit (instead of n -bit) accuracy

Published in:

Computer and Communication Systems, 1990. IEEE TENCON'90., 1990 IEEE Region 10 Conference on

Date of Conference:

24-27 Sep 1990