Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150801
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dc.contributor.authorChen, Yien_US
dc.contributor.authorWang, Zhengen_US
dc.contributor.authorPatil, Aakashen_US
dc.contributor.authorBasu, Arindamen_US
dc.date.accessioned2021-08-02T01:36:20Z-
dc.date.available2021-08-02T01:36:20Z-
dc.date.issued2019-
dc.identifier.citationChen, Y., Wang, Z., Patil, A. & Basu, A. (2019). A 2.86-TOPS/W current mirror cross-bar-based machine-learning and physical unclonable function engine for Internet-of-Things applications. IEEE Transactions On Circuits and Systems I: Regular Papers, 66(6), 2240-2252. https://dx.doi.org/10.1109/TCSI.2018.2889779en_US
dc.identifier.issn1549-8328en_US
dc.identifier.other0000-0002-4416-554X-
dc.identifier.other0000-0003-2855-9570-
dc.identifier.other0000-0003-1035-8770-
dc.identifier.urihttps://hdl.handle.net/10356/150801-
dc.description.abstractEnergy-efficient machine-learning and physical unclonable function (PUF) has drawn significant attention for Internet-of-Things (IoT) application in wake-up detection for bandwidth/computation reduction and privacy protection at sensor node or autonomous device. A machine-learning and PUF engine for IoT applications is presented in this paper with a current mirror cross-bar (CMCB) being a shared core circuit for both functions, leading to reduction in overhead area by 48.5 ×. A novel dimension expansion technique is proposed to increase weight matrix dimension beyond the physically implemented array with small hardware and energy overhead. A signed multiply-accumulation is realized in CMCB with differential current path and 2-phase conversion. The proposed engine achieves an error rate of 6.34% on MNIST digit recognition task with an energy efficiency of 2.86 TOPS/W. The PUF achieves a native bit error rate of 2.3% across corners and extremely low area per challenge response pair (CRP) of 4.17 × 10⁻⁵⁹ μm² /CRP due to exponentially more CRP enabled by ternary input mode.en_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Transactions on Circuits and Systems I: Regular Papersen_US
dc.rights© 2019 IEEE. All rights reserved.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleA 2.86-TOPS/W current mirror cross-bar-based machine-learning and physical unclonable function engine for Internet-of-Things applicationsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.researchVIRTUS, IC Design Centre of Excellenceen_US
dc.identifier.doi10.1109/TCSI.2018.2889779-
dc.identifier.scopus2-s2.0-85065878033-
dc.identifier.issue6en_US
dc.identifier.volume66en_US
dc.identifier.spage2240en_US
dc.identifier.epage2252en_US
dc.subject.keywordsMachine Learningen_US
dc.subject.keywordsCo-processoren_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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