Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/106809
Title: Low-power, adaptive neuromorphic systems : recent progress and future directions
Authors: Basu, Arindam
Acharya, Jyotibdha
Karnik, Tanay
Liu, Huichu
Li, Hai
Seo, Jae-Sun
Song, Chang
Keywords: Neuromorphics
Engineering::Electrical and electronic engineering
Hardware
Issue Date: 2018
Source: Basu, A., Acharya, J., Karnik, T., Liu, H., Li, H., Seo, J.-S., & Song, C. (2018). Low-power, adaptive neuromorphic systems : recent progress and future directions. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 8(1), 6-27. doi:10.1109/JETCAS.2018.2816339
Series/Report no.: IEEE Journal of Emerging and Selected Topics in Circuits and Systems
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.
URI: https://hdl.handle.net/10356/106809
http://hdl.handle.net/10220/49651
ISSN: 2156-3357
DOI: 10.1109/JETCAS.2018.2816339
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/JETCAS.2018.2816339
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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