Abstract
VLSI Artificial Neural Networks (ANNs) can be implemented with digital or analog technology. Digital implementations allow high precision and flexibility but to build effective neuromorphic systems it is necessary to exploit all available features and possible modes of operation of the MOS transistor in analog VLSI implementations: the neural computation is mapped directly on the electrical variables that describe the state of the network. In this way the physical restriction on the density of wires, the low power consumption of CMOS circuits (e.g. in the subthreshold domain), the limited precision and the cost of communication imposed by the spatial layout of electronic circuits are similar to the constraints imposed on biological networks (Mead 1989, Schwartz et al 1989, Mead 1990). This choice in favor of analog neural networks is also favored by the development of computational paradigms in computer vision research based on analog models (e.g. minimization of a functional that characterize the degree of acceptability of a solution according to the existing constraints) (Poggio et al 1985, Bertero et al 1988).
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Raffo, L., Bisio, G.M., Caviglia, D.D., Indiveri, G., Sabatini, S.P. (1994). A Multi-Layer Analog VLSI Architecture for Texture Analysis Isomorphic to Cortical Cells in Mammalian Visual System. In: Delgado-Frias, J.G., Moore, W.R. (eds) VLSI for Neural Networks and Artificial Intelligence. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-1331-9_6
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