Title :
O(n) depth-2 binary addition with feedforward neural nets
Author :
Vassiliadis, S. ; Bertels, K. ; Pechanek, G.G.
Author_Institution :
IBM Corp., Austin, TX, USA
fDate :
27 Jun-2 Jul 1994
Abstract :
In this paper we investigate the reduction of the size of depth-2 feedforward neural networks performing binary addition and related functions. We suggest that 2-1 binary n-bit addition and some related functions can be computed in a depth-2 network of size O(n) with maximum fan-in of 2n+1. Furthermore, we show, if both input polarities are available, that the comparison can be computed in a depth-1 network of size O(1) also with maximum fan-in of 2n+1
Keywords :
Boolean functions; computational complexity; feedforward neural nets; Boolean function; depth-1 network; depth-2 binary addition; depth-2 network; feedforward neural nets; input polarities; Boolean functions; Circuits; Computer networks; Feedforward neural networks; Microelectronics; Neural networks; Neurons; Polynomials; Reduced instruction set computing; Size measurement;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374487
