Optical and systolic implementation of an artificial neural network

The optical implementation of a locally interconnected systolic architecture of a neural network is considered in this paper. In the design presented here, the Hopfield model, one of the widely researched artificial neural network, is formulated as a consecutive matrix-vector multiplication problem with some prespecified threshold operations. The multiplication array structure is derived from a cascaded dependence graph with nonlinear assignment. By the same nonlinear assignment, a locally interconnected systolic array with bidirectional communicational links is then obtained. Each processing element in the systolic array is treated as a neuron and the synaptic strengths are stored in it. The optical design employs a liquid crystal light valve (LCLV) structure to implement the matrix-vector multiplier. The paper will show that the optical and systolic implementation of the neural networks achieves a higher precision in computation.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.