A real-time 17-scale object detection accelerator with adaptive 2000-stage classification in 65nm CMOS

Minkyu Kim; Abinash Mohanty; Deepak Kadetotad; Naveen Suda; Luning Wei; Pooja Saseendran; Xiaofei He; Yu Cao; Jae Sun Seo

doi:10.1109/ISCAS.2017.8050798

A real-time 17-scale object detection accelerator with adaptive 2000-stage classification in 65nm CMOS

Minkyu Kim, Abinash Mohanty, Deepak Kadetotad, Naveen Suda, Luning Wei, Pooja Saseendran, Xiaofei He, Yu Cao, Jae Sun Seo

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

Abstract

This paper presents an object detection accelerator that features many-scale (17), many-object (up to 50), multi-class (e.g., face, traffic sign), and high accuracy (average precision of 0.79/0.65 for AFW/BTSD datasets). Employing 10 gradient/color channels, integral features are extracted, and the results of 2,000 simple classifiers for rigid boosted templates are adaptively combined to make a strong classification. By jointly optimizing the algorithm and the hardware architecture, the prototype chip implemented in 65nm CMOS demonstrates real-time object detection of 13-35 frames per second with low power consumption of 22-160mW at 0.58-1.0V supply.

Original language	English (US)
Title of host publication	IEEE International Symposium on Circuits and Systems
Subtitle of host publication	From Dreams to Innovation, ISCAS 2017 - Conference Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467368520
DOIs	https://doi.org/10.1109/ISCAS.2017.8050798
State	Published - Sep 25 2017
Externally published	Yes
Event	50th IEEE International Symposium on Circuits and Systems, ISCAS 2017 - Baltimore, United States Duration: May 28 2017 → May 31 2017

Publication series

Name	Proceedings - IEEE International Symposium on Circuits and Systems
ISSN (Print)	0271-4310

Other

Other	50th IEEE International Symposium on Circuits and Systems, ISCAS 2017
Country/Territory	United States
City	Baltimore
Period	5/28/17 → 5/31/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

classification
low-power
machine learning
object detection
real-time
special-purpose accelerator

Publisher link

10.1109/ISCAS.2017.8050798

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Kim, M., Mohanty, A., Kadetotad, D., Suda, N., Wei, L., Saseendran, P., He, X., Cao, Y., & Seo, J. S. (2017). A real-time 17-scale object detection accelerator with adaptive 2000-stage classification in 65nm CMOS. In IEEE International Symposium on Circuits and Systems: From Dreams to Innovation, ISCAS 2017 - Conference Proceedings Article 8050798 (Proceedings - IEEE International Symposium on Circuits and Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCAS.2017.8050798

A real-time 17-scale object detection accelerator with adaptive 2000-stage classification in 65nm CMOS. / Kim, Minkyu; Mohanty, Abinash; Kadetotad, Deepak et al.
IEEE International Symposium on Circuits and Systems: From Dreams to Innovation, ISCAS 2017 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. 8050798 (Proceedings - IEEE International Symposium on Circuits and Systems).

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

Kim, M, Mohanty, A, Kadetotad, D, Suda, N, Wei, L, Saseendran, P, He, X, Cao, Y & Seo, JS 2017, A real-time 17-scale object detection accelerator with adaptive 2000-stage classification in 65nm CMOS. in IEEE International Symposium on Circuits and Systems: From Dreams to Innovation, ISCAS 2017 - Conference Proceedings., 8050798, Proceedings - IEEE International Symposium on Circuits and Systems, Institute of Electrical and Electronics Engineers Inc., 50th IEEE International Symposium on Circuits and Systems, ISCAS 2017, Baltimore, United States, 5/28/17. https://doi.org/10.1109/ISCAS.2017.8050798

Kim M, Mohanty A, Kadetotad D, Suda N, Wei L, Saseendran P et al. A real-time 17-scale object detection accelerator with adaptive 2000-stage classification in 65nm CMOS. In IEEE International Symposium on Circuits and Systems: From Dreams to Innovation, ISCAS 2017 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. 8050798. (Proceedings - IEEE International Symposium on Circuits and Systems). doi: 10.1109/ISCAS.2017.8050798

Kim, Minkyu ; Mohanty, Abinash ; Kadetotad, Deepak et al. / A real-time 17-scale object detection accelerator with adaptive 2000-stage classification in 65nm CMOS. IEEE International Symposium on Circuits and Systems: From Dreams to Innovation, ISCAS 2017 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. (Proceedings - IEEE International Symposium on Circuits and Systems).

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abstract = "This paper presents an object detection accelerator that features many-scale (17), many-object (up to 50), multi-class (e.g., face, traffic sign), and high accuracy (average precision of 0.79/0.65 for AFW/BTSD datasets). Employing 10 gradient/color channels, integral features are extracted, and the results of 2,000 simple classifiers for rigid boosted templates are adaptively combined to make a strong classification. By jointly optimizing the algorithm and the hardware architecture, the prototype chip implemented in 65nm CMOS demonstrates real-time object detection of 13-35 frames per second with low power consumption of 22-160mW at 0.58-1.0V supply.",

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AU - Saseendran, Pooja

AU - He, Xiaofei

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AB - This paper presents an object detection accelerator that features many-scale (17), many-object (up to 50), multi-class (e.g., face, traffic sign), and high accuracy (average precision of 0.79/0.65 for AFW/BTSD datasets). Employing 10 gradient/color channels, integral features are extracted, and the results of 2,000 simple classifiers for rigid boosted templates are adaptively combined to make a strong classification. By jointly optimizing the algorithm and the hardware architecture, the prototype chip implemented in 65nm CMOS demonstrates real-time object detection of 13-35 frames per second with low power consumption of 22-160mW at 0.58-1.0V supply.

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A real-time 17-scale object detection accelerator with adaptive 2000-stage classification in 65nm CMOS

Abstract

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Keywords

Publisher link

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