Title :
Is VLSI neural learning robust against circuit limitations?
Author :
Card, H.C. ; Dolenko, B.K. ; McNeill, D.K. ; Schneider, C.R. ; Schneider, R.S.
Author_Institution :
Dept. of Electr. & Comput. Eng., Manitoba Univ., Winnipeg, Man., Canada
fDate :
27 Jun-2 Jul 1994
Abstract :
An investigation is made of the tolerance of various in-circuit learning algorithms to component imprecision and other circuit limitations in artificial neural networks. Supervised learning mechanisms including backpropagation and contrastive Hebbian leaning, and unsupervised soft competitive learning are all shown to be tolerant of those levels of arithmetic inaccuracy, noise, nonlinearity, weight decay, and statistical variation from fabrication that the authors have experienced in 1.2 μm analog CMOS circuits employing Gilbert multipliers as the primary computational element. These learning circuits also function properly in the presence of offset errors in analog multipliers and adders, provided that the computed weight updates are constrained by the circuitry to be made only when they exceed certain minimum or threshold values. These results are also relevant for compact (low bit rate) digital implementations
Keywords :
CMOS analogue integrated circuits; Hebbian learning; analogue multipliers; backpropagation; learning (artificial intelligence); neural chips; unsupervised learning; 1.2 μm analog CMOS circuits; Gilbert multipliers; VLSI neural learning; analog adders; analog multipliers; arithmetic inaccuracy; artificial neural networks; backpropagation; circuit limitations; component imprecision; contrastive Hebbian leaning; in-circuit learning algorithms; learning circuits; noise; nonlinearity; offset errors; statistical variation; supervised learning mechanisms; tolerance; unsupervised soft competitive learning; weight decay; Analog computers; Arithmetic; Artificial neural networks; Backpropagation algorithms; Circuit noise; Fabrication; Noise level; Robustness; Supervised learning; Very large scale integration;
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.1994.374447