An Optical Implementation of Associative Memory Based on Inner Product Neural Network Model

In this paper, we propose a hybrid optical/digital version of the associative memory which improve hardware efficiency and increase convergence rates. Multifocus hololens are used as space-varient optical element for performing inner product and summation function. The real-time input and the stored states of memory matrix is formated using LCTV. One method of adaptively changing the weights of stored vectors during each iteration is implemented electronically. A design for a optical implementation scheme is discussed and the proposed architecture is demonstrated the ability of retrieving with computer simmulation.