Analog CMOS neural networks based on Gilbert multipliers with in-circuit learning

This paper examines analog CMOS circuit implementations of several common neural network algorithms. All circuits described perform in-circuit learning, using Gilbert multipliers as a primary circuit component. These include 3 /spl mu/m and 1.2 /spl mu/m designs for contrastive Hebbian learning, and Becker-Hinton networks (a variation of delta-rule learning). In addition, unsupervised learning circuits for competitive learning are presented. >