A modular architecture for hybrid VLSI neural networks and its application in a smart photosensor

Abstract

We describe a modular architecture for the VLSI implementation of multilayer neural networks using a universal hybrid building block. Based on this approach, a programmable smart photosensor is designed which is in fact a VLSI realization of a multilayer feedforward neural network with an integrated photoreceptor array using 1.2 /spl mu/m CMOS technology. Each universal building block in this architecture comprises a multiplying DAC synapse, a portion of a nonlinear distributed neuron and compact digital registers for programming and storing a synaptic weight. The proposed modular neural network architecture features design simplicity and scalability, area efficiency, reduced interconnection problems and increased robustness. Based on this architecture and using cell-level optimization, the synaptic density in this version of the neural-based smart sensor has been increased by a factor of two. This has lead to an increase in the area available for a larger and higher resolution optical input array.