Analog Very Large Scale Integration (VLSI) Implementations of Artificial Neural Networks
A comparison of the different implementation techniques is provided, as is the type of paradigm implemented (e.g., backpropagation, hopfield, bidirectional associative memories, etc.).
Abstract
Abstract : There has been a recent resurgence of interest in the multi- disciplinary field of artificial neural networks. Artificial neural networks, originally inspired by the computational capabilities of the human brain, refer to a variety of computing architectures that consist of massively parallel interconnections of simple processing elements. Currently, there exist two promising advanced technologies for implementing neural networks: Very Large Scale Integrated (VLSI) circuits and optical. This final technical report describes the utilization of VLSI circuits for implementing various neural networks, with an emphasis on analog VLSI. A comparison of the different implementation techniques is provided, as is the type of paradigm implemented (e.g., backpropagation, hopfield, bidirectional associative memories, etc.). Artificial Neural Networks, Analog VLSI.