Learning in neural networks: VLSI implementation strategies
Tuan Anh Duong, Silvio P. Eberhardt, Taher Daud, A. P. Thakoor · McGraw-Hill, Inc. eBooks · 1996
Fully-parallel hardware neural network implementations may be applied to high-speed recognition, classification, and mapping tasks in areas such as vision, or can be used as low-cost self-contained units for tasks such as error detection in mechanical systems (e.g. autos). Learning is required not only to satisfy application requirements, but also to overcome hardware-imposed limitations such as reduced dynamic range of connections.