Nanoelectronic neurocomputing: Status and prospects

L. Ceze, J. Hasler, K. K. Likharev, Jae-sun Seo, T. Sherwood, D. Strukov, Y. Xie, Shimeng Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations


Potential advantages of specialized hardware for neuromorphic computing had been recognized several decades ago (see, e.g., Refs. [1, 2]), but the need for it became especially acute recently, due to significant advances of the computational neuroscience and machine learning. The most vivid example is given by the deep learning in convolution neuromorphic networks [3]: the recent dramatic progress of this technology, with it's rapid extension to several important applications, was enabled by the use of modern GPU clusters [4, 5]. Even higher performance and lower power consumption has been recently demonstrated using FPGAS [5-7] and custom digital circuits [5, 8].

Original languageEnglish (US)
Title of host publication74th Annual Device Research Conference, DRC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509028276
StatePublished - Aug 22 2016
Event74th Annual Device Research Conference, DRC 2016 - Newark, United States
Duration: Jun 19 2016Jun 22 2016


Other74th Annual Device Research Conference, DRC 2016
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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