Run-time performance and power optimization of parallel disparity estimation on many-core platforms

Leech, Charles and Vala, Charan Kumar and Acharyya, Amit and Yang, Sheng and Merrett, Geoffrey and Al-Hashimi, Bashir (2017) Run-time performance and power optimization of parallel disparity estimation on many-core platforms. ACM Transactions on Embedded Computing Systems. ISSN 1539-9087 (In Press)

[img]
Preview
Text
acmtecs.pdf - Accepted Version

Download (2MB) | Preview

Abstract

This paper investigates the use of many-core systems to execute the disparity estimation algorithm, used in stereo vision applications, as these systems can provide flexibility between performance scaling and power consumption. We present a learning-based run-time management approach which achieves a required performance threshold whilst minimizing power consumption through dynamic control of frequency and core allocation. Experimental results are obtained from a 61-core Intel Xeon Phi platform for the above investigation. The same performance can be achieved with an average reduction in power consumption of 27.8% and increased energy efficiency by 30.04% when compared to DVFS control alone without run-time management.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Acharyya, AmitUNSPECIFIED
Item Type: Article
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: Team Library
Date Deposited: 29 Aug 2017 09:21
Last Modified: 29 Aug 2017 09:21
URI: http://raiith.iith.ac.in/id/eprint/3509
Publisher URL:
OA policy: http://www.sherpa.ac.uk/romeo/issn/1539-9087/
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 3509 Statistics for this ePrint Item