Application of highly parallel computing hardware to pattern recognition problems

Neural networks are well known for their ability to perform pattern recognition tasks. This paper discusses the use of parallel neural network hardware for performing pattern recognition tasks. We address the need for neural network hardware and how it can dramatically improve system performance both in training and in actual application. The use of specialized parallel processing hardware is discussed as well as alternative hardware and software approaches. Finally we give some comparisons between multiprocessor computer architecture, Pentium class microcomputers and custom hardware.