This paper describes a modular analog VLSI architecture for the implementation of artificial neural networks. Analog neural network implementations are faster and smaller than their digital counterparts, but the problem of smaller dynamic range of the analog weight memory and the linearity of the synapses based on analog multipliers increases the need for design effort at the circuit level. We suggest that a complex neural network system can be implemented in a single chip if a modular architecture design using simple analog circuits is followed. To demonstrate the VLSI implementability of the neural network system, a description of each analog circuit block is provided.
CITATION STYLE
Vermesan, O. (1995). A modular VLSI architecture for neural networks implementation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 930, pp. 794–799). Springer Verlag. https://doi.org/10.1007/3-540-59497-3_252
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