Abstract:
Recent developments of unconventional hardware using memristors and atomic switch networks has led to renewed interest in hardware neuromorphic solutions. Most hardware m...Show MoreMetadata
Abstract:
Recent developments of unconventional hardware using memristors and atomic switch networks has led to renewed interest in hardware neuromorphic solutions. Most hardware models rely upon a reservoir neural network as the basis of any learning, but the distinct differences between software implementations and hardware reality mean what we take for granted in traditional software reservoirs - such as cycles, loops, infinite energy, and discrete time - may be severely limited or unavailable in hardware, raising questions about how a hardware implementation would perform and how to potentially overcome these limitations. Proposed hardware additions, such as an echoer or an input delay mechanism, address some of these limitations.
Date of Conference: 04-06 December 2017
Date Added to IEEE Xplore: 05 July 2018
ISBN Information:
Electronic ISSN: 2151-2205