Artificial neural networks in hardware A survey of two decades of progress

This article presents a comprehensive overview of the hardware realizations of artificial neural network (ANN) models known as hardware neural networks (HNN) appearing in academic studies as prototypes as well as in commercial use HNN research has witnessed a steady progress for more than last two decades though commercial adoption of the technology has been relatively slower We study the overall progress in the field across all major ANN models hardware design approaches and applications We outline underlying design approaches for mapping an ANN model onto a compact reliable and energy efficient hardware entailing computation and communication and survey a wide range of illustrative examples Chip design approaches (digital analog hybrid and FPGA based) at neuronal level and as neurochips realizing complete ANN models are studied We specifically discuss in detail neuromorphic designs including spiking neural network hardware cellular neural network implementations reconfigurable FPGA based implementations in particular for stochastic ANN models and optical implementations Parallel digital implementations employing bit-slice systolic and SIMD architectures implementations for associative neural memories and RAM based implementations are also outlined We trace the recent trends and explore potential future research directions (C) 2010 Elsevier BV All rights reserved