Low-Power, Adaptive Neuromorphic Systems: Recent Progress and Future Directions

Arindam Basu, Jyotibdha Acharya, Tanay Karnik, Huichu Liu, Hai Li, Jae-sun Seo, Chang Song

Research output: Contribution to journalArticle

21 Scopus citations

Abstract

In this paper, we present a survey of recent works in developing “neuromorphic” or neuro-inspired hardware systems. In particular, we focus on those systems which can either learn from data in an unsupervised or online supervised manner. We present algorithms and architectures developed specially to support on-chip learning. Emphasis is placed on hardware friendly modifications of standard algorithms such as backpropagation as well as novel algorithms such as structural plasticity developed specially for low-resolution synapses. We cover works related to both spike based and more traditional non-spike based algorithms. This is followed by developments in novel devices such as floating-gate MOS, memristors and spintronic devices. CMOS circuit innovations for on-chip learning and CMOS interface circuits for post-CMOS devices such as memristors are presented. Common architectures such as crossbar or island style arrays are discussed along with their relative merits and demerits. Finally, we present some possible applications of neuromorphic hardware such as brain-machine interfaces, robotics, etc. and identify future research trends in the field.

Original languageEnglish (US)
JournalIEEE Journal on Emerging and Selected Topics in Circuits and Systems
DOIs
StateAccepted/In press - Mar 14 2018
Externally publishedYes

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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