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|Title:||A 2.86-TOPS/W current mirror cross-bar-based machine-learning and physical unclonable function engine for Internet-of-Things applications||Authors:||Chen, Yi
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2019||Source:||Chen, 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.2018.2889779||Journal:||IEEE Transactions on Circuits and Systems I: Regular Papers||Abstract:||Energy-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.||URI:||https://hdl.handle.net/10356/150801||ISSN:||1549-8328||DOI:||10.1109/TCSI.2018.2889779||Rights:||© 2019 IEEE. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Journal Articles|
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