Neural Computation of Arithmetic Functions

Abstract

The basic processing unit of a neural network is a linear threshold element. It has been known that neural networks can be much more powerful than traditional logic circuits, assuming that each threshold element can be built at a cost comparable to that of and, or, not logic elements. 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. We improve some known results by showing that the product of two n-bit numbers and sorting of n n-bit numbers can be computed by a polynomial-size neural network using only 4 and 5 unit delays, respectively. Moreover, the weights of each threshold element in our neural networks require O(log n)-bit (instead of n-bit) accuracy. © 1990, IEEE