Energy-Efficient Edge Inference on Multi-Channel Streaming Data in 28nm HKMG FeFET Technology

S. Dutta; W. Chakraborty; J. Gomez; K. Ni; S. Joshi; S. Datta

doi:10.23919/VLSIT.2019.8776525

Energy-Efficient Edge Inference on Multi-Channel Streaming Data in 28nm HKMG FeFET Technology

S. Dutta, W. Chakraborty, J. Gomez, K. Ni, S. Joshi, S. Datta

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

We present a system implementing extremely energy-efficient inference on multi-channel biomedical-sensor data. We leverage Ferroelectric FET (FeFET) to perform classification directly on analog sensor signals. We demonstrate: (i) voltage-controlled multi-domain ferroelectric polarization switching to obtain 8 distinct transconductance (g_m) states in a 28nm HKMG FeFET technology [1], (ii) 30x tunable range in g_m over the bandwidth of interest, (iii) successful implementation of artifact removal, feature extraction and classification for seizure detection from CHB-MIT EEG dataset with 98.46% accuracy and < 0.375/hr. false alarm rate for two patients, (iv) ultra-low energy of 47 fJ/MAC with 1,000x improvement in area compared to alternative mixed-signal MAC.

Original language	English
Title of host publication	2019 Symposium on VLSI Technology, VLSI Technology 2019 - Digest of Technical Papers
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	T38-T39
ISBN (Electronic)	9784863487178
DOIs	https://doi.org/10.23919/VLSIT.2019.8776525
State	Published - Jun 2019
Externally published	Yes
Event	39th Symposium on VLSI Technology, VLSI Technology 2019 - Kyoto, Japan Duration: 9 Jun 2019 → 14 Jun 2019

Publication series

Name	Digest of Technical Papers - Symposium on VLSI Technology
Volume	2019-June
ISSN (Print)	0743-1562

Conference

Conference	39th Symposium on VLSI Technology, VLSI Technology 2019
Country/Territory	Japan
City	Kyoto
Period	9/06/19 → 14/06/19

Bibliographical note

Publisher Copyright:
© 2019 The Japan Society of Applied Physics.

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10.23919/VLSIT.2019.8776525

Cite this

Dutta, S., Chakraborty, W., Gomez, J., Ni, K., Joshi, S., & Datta, S. (2019). Energy-Efficient Edge Inference on Multi-Channel Streaming Data in 28nm HKMG FeFET Technology. In 2019 Symposium on VLSI Technology, VLSI Technology 2019 - Digest of Technical Papers (pp. T38-T39). Article 8776525 (Digest of Technical Papers - Symposium on VLSI Technology; Vol. 2019-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/VLSIT.2019.8776525

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title = "Energy-Efficient Edge Inference on Multi-Channel Streaming Data in 28nm HKMG FeFET Technology",

abstract = "We present a system implementing extremely energy-efficient inference on multi-channel biomedical-sensor data. We leverage Ferroelectric FET (FeFET) to perform classification directly on analog sensor signals. We demonstrate: (i) voltage-controlled multi-domain ferroelectric polarization switching to obtain 8 distinct transconductance (gm) states in a 28nm HKMG FeFET technology [1], (ii) 30x tunable range in gm over the bandwidth of interest, (iii) successful implementation of artifact removal, feature extraction and classification for seizure detection from CHB-MIT EEG dataset with 98.46\% accuracy and < 0.375/hr. false alarm rate for two patients, (iv) ultra-low energy of 47 fJ/MAC with 1,000x improvement in area compared to alternative mixed-signal MAC.",

author = "S. Dutta and W. Chakraborty and J. Gomez and K. Ni and S. Joshi and S. Datta",

note = "Publisher Copyright: {\textcopyright} 2019 The Japan Society of Applied Physics.; 39th Symposium on VLSI Technology, VLSI Technology 2019 ; Conference date: 09-06-2019 Through 14-06-2019",

year = "2019",

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doi = "10.23919/VLSIT.2019.8776525",

language = "English",

series = "Digest of Technical Papers - Symposium on VLSI Technology",

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Dutta, S, Chakraborty, W, Gomez, J, Ni, K, Joshi, S & Datta, S 2019, Energy-Efficient Edge Inference on Multi-Channel Streaming Data in 28nm HKMG FeFET Technology. in 2019 Symposium on VLSI Technology, VLSI Technology 2019 - Digest of Technical Papers., 8776525, Digest of Technical Papers - Symposium on VLSI Technology, vol. 2019-June, Institute of Electrical and Electronics Engineers Inc., pp. T38-T39, 39th Symposium on VLSI Technology, VLSI Technology 2019, Kyoto, Japan, 9/06/19. https://doi.org/10.23919/VLSIT.2019.8776525

Energy-Efficient Edge Inference on Multi-Channel Streaming Data in 28nm HKMG FeFET Technology. / Dutta, S.; Chakraborty, W.; Gomez, J. et al.
2019 Symposium on VLSI Technology, VLSI Technology 2019 - Digest of Technical Papers. Institute of Electrical and Electronics Engineers Inc., 2019. p. T38-T39 8776525 (Digest of Technical Papers - Symposium on VLSI Technology; Vol. 2019-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Datta, S.

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AB - We present a system implementing extremely energy-efficient inference on multi-channel biomedical-sensor data. We leverage Ferroelectric FET (FeFET) to perform classification directly on analog sensor signals. We demonstrate: (i) voltage-controlled multi-domain ferroelectric polarization switching to obtain 8 distinct transconductance (gm) states in a 28nm HKMG FeFET technology [1], (ii) 30x tunable range in gm over the bandwidth of interest, (iii) successful implementation of artifact removal, feature extraction and classification for seizure detection from CHB-MIT EEG dataset with 98.46% accuracy and < 0.375/hr. false alarm rate for two patients, (iv) ultra-low energy of 47 fJ/MAC with 1,000x improvement in area compared to alternative mixed-signal MAC.

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Dutta S, Chakraborty W, Gomez J, Ni K, Joshi S, Datta S. Energy-Efficient Edge Inference on Multi-Channel Streaming Data in 28nm HKMG FeFET Technology. In 2019 Symposium on VLSI Technology, VLSI Technology 2019 - Digest of Technical Papers. Institute of Electrical and Electronics Engineers Inc. 2019. p. T38-T39. 8776525. (Digest of Technical Papers - Symposium on VLSI Technology). doi: 10.23919/VLSIT.2019.8776525

Energy-Efficient Edge Inference on Multi-Channel Streaming Data in 28nm HKMG FeFET Technology

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