Body bias optimization for variable pipelined CGRA

Takuya Kojima, Naoki Ando, Hayate Okuhara, Ng Anh Vu Doan, Hideharu Amano

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Variable Pipeline Cool Mega Array (VPCMA) is an low power Coarse Grained Reconfigurable Architecture (CGRA) based on the concept of CMA (Cool Mega Array). It implements a pipeline structure that can be configured depending on performance requirements, and the silicon on thin buried oxide (SOTB) technology that allows to control its body bias voltage to balance performance and leakage power. In this paper, we propose a methodology to optimize exactly with an Integer Linear Program the VPCMA body bias while considering simultaneously its variable pipeline structure. For the studied applications, we evaluate that it is possible to achieve an average reduction of energy consumption of 19.3% and 11.8% when compared to respectively the zero bias (without body bias control) and the uniform (control of the whole PE array) cases, while respecting performance constraints. Besides, with appropriate body bias control, it is possible to extend the possible performance, hence enabling broader trade-off analyzes between consumption and performance. These promising results show that applying an adequate optimization technique for the body bias control while simultaneously considering pipeline structures can not only enable further power reduction than previous methods, but also allow more trade-off analysis possibilities.

Original languageEnglish
Title of host publication2017 27th International Conference on Field Programmable Logic and Applications, FPL 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9789090304281
DOIs
Publication statusPublished - 2017 Oct 2
Event27th International Conference on Field Programmable Logic and Applications, FPL 2017 - Gent, Belgium
Duration: 2017 Sep 42017 Sep 6

Other

Other27th International Conference on Field Programmable Logic and Applications, FPL 2017
CountryBelgium
CityGent
Period17/9/417/9/6

Fingerprint

Reconfigurable architectures
Pipelines
Bias voltage
Energy utilization
Silicon
Oxides

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Software

Cite this

Kojima, T., Ando, N., Okuhara, H., Doan, N. A. V., & Amano, H. (2017). Body bias optimization for variable pipelined CGRA. In 2017 27th International Conference on Field Programmable Logic and Applications, FPL 2017 [8056851] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/FPL.2017.8056851

Body bias optimization for variable pipelined CGRA. / Kojima, Takuya; Ando, Naoki; Okuhara, Hayate; Doan, Ng Anh Vu; Amano, Hideharu.

2017 27th International Conference on Field Programmable Logic and Applications, FPL 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 8056851.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kojima, T, Ando, N, Okuhara, H, Doan, NAV & Amano, H 2017, Body bias optimization for variable pipelined CGRA. in 2017 27th International Conference on Field Programmable Logic and Applications, FPL 2017., 8056851, Institute of Electrical and Electronics Engineers Inc., 27th International Conference on Field Programmable Logic and Applications, FPL 2017, Gent, Belgium, 17/9/4. https://doi.org/10.23919/FPL.2017.8056851
Kojima T, Ando N, Okuhara H, Doan NAV, Amano H. Body bias optimization for variable pipelined CGRA. In 2017 27th International Conference on Field Programmable Logic and Applications, FPL 2017. Institute of Electrical and Electronics Engineers Inc. 2017. 8056851 https://doi.org/10.23919/FPL.2017.8056851
Kojima, Takuya ; Ando, Naoki ; Okuhara, Hayate ; Doan, Ng Anh Vu ; Amano, Hideharu. / Body bias optimization for variable pipelined CGRA. 2017 27th International Conference on Field Programmable Logic and Applications, FPL 2017. Institute of Electrical and Electronics Engineers Inc., 2017.
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