Abstract
Though artificial neural networks have been studied for five decades, they have experienced, in the past ten years, a very rapidly growing interest. Most of the applications in this domain are however either simulated on conventional machines or implemented on some specialized hardware dedicated to a given model. At the present day, there is no platform which is, at the same time, versatile enough to implement any neural-network model and learning rule, and fast enough to be used on large problems. This fact prompted many researchers to work toward the design of a generic neural-network computer (Treleaven et al 1989). As an intermediate step in this quest, a multi-model hardware implementation, whose architecture is influenced by biological considerations, is presented in this paper.
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Viredaz, M.A., Lehmann, C., Blayo, F., Ienne, P. (1994). MANTRA: A Multi-Model Neural-Network Computer. 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_9
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DOI: https://doi.org/10.1007/978-1-4899-1331-9_9
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