Abstract

Neural network modules based on page-oriented dynamic digital photorefractive memory are described. The modules can implement two different interconnection organizations, fan-out and fan-in, depending on their target network applications. Neural network learning is realized by the real-time memory update of dynamic digital photorefractive memory. Physical separation of subvolumes in the page-oriented photorefractive memory architecture contributes to the low cross talk and high diffraction efficiency of the stored interconnection weights. Digitally encoded interconnection weights ensure high accuracy, providing superior neural network system scalability. Module scalability and feedforward throughput have been investigated based on photorefractive memory geometry and the photodetector power requirements. The following four approaches to extend module scalability are discussed: partial optical summation, semiparallel feedforward operation, time partitioning, and interconnection matrix partitioning. Learning capabilities of the system are investigated in terms of required interconnection primitives for implementing learning processes and three memory-update schemes. The experimental results of Perceptron learning network implementation with 900 input neurons with digital 6-bit accuracy are reported.

© 1996 Optical Society of America

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