Authors: S.E. Lyshevski
Affilation: Rochester Institute of Technology, United States
Pages: 690 - 693
Keywords: neuromorphic processing, computing, robust
This paper contributes to robust fault-tolerant neuromorphic computing for expected nano-centric processing hardware. We develop: (a) A library of neuromorphic networks for elementary logic function in order to enable the design of an arbitrary complexity logic networks; (b) Methods for designing fault tolerant neuromorphic networks in the presence of unreliable or failed threshold elements in the network. Our interest to neuromorphic computing is motivated by the stochastic and quantum physics of phenomena in microscopic (nano) processing primitives.
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