Vlsi Devices and Circuits for Neural Networks

This paper provides a tutorial of various VLSI approaches to synthesizing artificial neural networks as microelectronic systems. The means by which the network learns and the synaptic weights become modified is a central theme in this study. The majority of the presentation is concerned with analog circuit approaches to neurons and synapses, employing CMOS circuits. Also included is recent work towards VLSI in situ learning circuits which implement qualitative approximations to Hebbian learning with economy of transistors. An attempt is also made to anticipate relevant developments in VLSI devices which would be suited to neural networks, just as conventional MOS transistors are well suited to traditional digital computer systems.