Optical inner-product implementations for multi-layer BAM with 2-dimensional patterns

The authors present an optical inner-product architecture for MBAM (multi-layer bidirectional associative memory) with two-dimensional input and output patterns. The proposed architecture utilizes compact solid modules for single-layer feedforward networks, which may be cascaded for MBAM. Instead of analog interconnection weights the inner-product scheme stores input and output patterns. For binary input and output patterns this inner-product scheme requires binary spatial light modulators only, and is scalable to very large-size implementations. Unlike optical neural networks for one-dimensional patterns, multifocus holograms and lenslet arrays become essential components in these modules. The performance of the MBAM was demonstrated by an electrooptic inner-product implementation for the exclusive-OR problem