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

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

6 Citas (Scopus)

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	2019 Symposium on VLSI Technology, VLSI Technology 2019 - Digest of Technical Papers
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	T38-T39
ISBN (versión digital)	9784863487178
DOI	https://doi.org/10.23919/VLSIT.2019.8776525
Estado	Publicada - jun. 2019
Publicado de forma externa	Sí
Evento	39th Symposium on VLSI Technology, VLSI Technology 2019 - Kyoto, Japón Duración: 9 jun. 2019 → 14 jun. 2019

Serie de la publicación

Nombre	Digest of Technical Papers - Symposium on VLSI Technology
Volumen	2019-June
ISSN (versión impresa)	0743-1562

Conferencia

Conferencia	39th Symposium on VLSI Technology, VLSI Technology 2019
País/Territorio	Japón
Ciudad	Kyoto
Período	9/06/19 → 14/06/19

Nota bibliográfica

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

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10.23919/VLSIT.2019.8776525

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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. En 2019 Symposium on VLSI Technology, VLSI Technology 2019 - Digest of Technical Papers (pp. T38-T39). Artículo 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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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",

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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. En 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, Japón, 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).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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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. En 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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