A survey of two decades of progress. Abstract. 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. Keywords. Hardware neural network; Neurochip; Parallel neural architecture; Digital neural design; Analog neural design; Hybrid neural design; Neuromorphic system; FPGA based ANN implementation; CNN implementation; RAM based implementation; Optical neural network. Copyright . All rights reserved. Janardan Misra received his B. Tech. From 2. 00. Texas Instruments India (Pvt.) Ltd, India. Thereafter he received his M. Sc. After a brief teaching tenure between 2. Research lab at HTS Research, Bangalore, India. Artificial neural network (ANN) seminar report/pdf/ppt. Artificial neural network (ANN) seminar report. A neural network model for prediction of sound quality. An artificial neural network is composed of many. A neural network model for prediction of sound. His research interests include ANN hardware, machine learning, artificial life, enterprise security, and formal methods. Indranil Saha received his B. Tech. From 2. 00. Research lab at Honeywell Technology Solutions, Bangalore, India. Presently, he is a graduate student at Computer Science Department of University of California, Los Angeles. His research interests include hardware neural networks, formal methods, and embedded software reliability. The neural network model to. Artificial neural networks.
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