Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016)

A Fast and Low Power Hardware Accelerator for ANN Working at Near Threshold Voltage

Authors
Tianbao Chen, Shouyi Yin, Shaojun Wei
Corresponding Author
Tianbao Chen
Available Online April 2016.
DOI
https://doi.org/10.2991/ameii-16.2016.134How to use a DOI?
Keywords
ANN, Hardware accelerator, NTV
Abstract

Arti cial neural network (ANN) are widely applied in machine learning and artificial intelligence. But ANN usually requires large data throughputs and induces high power consumptions. This paper proposes an accelerator design guideline for ANN with full consideration of the hardware scale, performance and power consumption. We apply multiple clocks in our design to get a high data throughput and introduce the near threshold voltage (NTV) to get a lower power consumption. We further optimize the multiplication operation in critical path obtaining a higher performance.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Volume Title
Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016)
Series
Advances in Engineering Research
Publication Date
April 2016
ISBN
978-94-6252-188-9
ISSN
2352-5401
DOI
https://doi.org/10.2991/ameii-16.2016.134How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Tianbao Chen
AU  - Shouyi Yin
AU  - Shaojun Wei
PY  - 2016/04
DA  - 2016/04
TI  - A Fast and Low Power Hardware Accelerator for ANN Working at Near Threshold Voltage
BT  - Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016)
PB  - Atlantis Press
SP  - 683
EP  - 687
SN  - 2352-5401
UR  - https://doi.org/10.2991/ameii-16.2016.134
DO  - https://doi.org/10.2991/ameii-16.2016.134
ID  - Chen2016/04
ER  -