The University of Southampton
University of Southampton Institutional Repository

Workload uncertainty characterization and adaptive frequency scaling for energy minimization of embedded systems

Workload uncertainty characterization and adaptive frequency scaling for energy minimization of embedded systems
Workload uncertainty characterization and adaptive frequency scaling for energy minimization of embedded systems
A primary design optimization objective for battery-operated embedded systems is to minimize the energy consumption of applications while satisfying their performance requirement. A system-level approach to this problem is to scale the frequency of the hardware based on the readings obtained from the hardware performance monitors. We show that the performance monitor readings contain uncertainty, which becomes prominent when applications are executed in a multicore environment. These uncertainties (termed as "noise") are attributed to factors such as cache contention and DRAM access time, that are very difficult to predict dynamically. In this paper, we propose a multinomial logistic regression model, which combines probabilistic interpretation with maximum likelihood (ML) estimation to classify an incoming noisy workload, at run-time, into a finite set of classes. Every workload class corresponds to a frequency pre-determined using an appropriate training set and results in minimum energy consumption. The classifier incorporates (1) "noise" with arbitrary probability distribution to estimate the actual frame workload; and (2) the frequency switching overhead, neither of which are considered in any of the existing approaches. The classified frequency is applied on the processing cores to execute the workload. The proposed approach is engineered into an embedded multicore system and is validated with a set of standard multimedia applications. Results demonstrate that the proposed approach minimizes energy consumption by an average 20% as compared to the existing techniques.
Das, Anup K.
2a0d6cea-309b-4053-a62e-234807f89306
Shafik, Rishad Ahmed
aa0bdafc-b022-4cb2-a8ef-4bf8a03ba524
Merrett, Geoff V.
89b3a696-41de-44c3-89aa-b0aa29f54020
Hashimi, B.M.
0b29c671-a6d2-459c-af68-c4614dce3b5d
Kumar, Akash
3e1191e9-dc51-4f9e-8e47-80524db219dc
Veeravalli, Bharadwaj
b836c94d-baad-450a-826b-84021f56db49
Das, Anup K.
2a0d6cea-309b-4053-a62e-234807f89306
Shafik, Rishad Ahmed
aa0bdafc-b022-4cb2-a8ef-4bf8a03ba524
Merrett, Geoff V.
89b3a696-41de-44c3-89aa-b0aa29f54020
Hashimi, B.M.
0b29c671-a6d2-459c-af68-c4614dce3b5d
Kumar, Akash
3e1191e9-dc51-4f9e-8e47-80524db219dc
Veeravalli, Bharadwaj
b836c94d-baad-450a-826b-84021f56db49

Das, Anup K., Shafik, Rishad Ahmed, Merrett, Geoff V., Hashimi, B.M., Kumar, Akash and Veeravalli, Bharadwaj (2015) Workload uncertainty characterization and adaptive frequency scaling for energy minimization of embedded systems At Conference on Design, Automation & Test in Europe, France. 09 - 13 Mar 2015. 6 pp.

Record type: Conference or Workshop Item (Paper)

Abstract

A primary design optimization objective for battery-operated embedded systems is to minimize the energy consumption of applications while satisfying their performance requirement. A system-level approach to this problem is to scale the frequency of the hardware based on the readings obtained from the hardware performance monitors. We show that the performance monitor readings contain uncertainty, which becomes prominent when applications are executed in a multicore environment. These uncertainties (termed as "noise") are attributed to factors such as cache contention and DRAM access time, that are very difficult to predict dynamically. In this paper, we propose a multinomial logistic regression model, which combines probabilistic interpretation with maximum likelihood (ML) estimation to classify an incoming noisy workload, at run-time, into a finite set of classes. Every workload class corresponds to a frequency pre-determined using an appropriate training set and results in minimum energy consumption. The classifier incorporates (1) "noise" with arbitrary probability distribution to estimate the actual frame workload; and (2) the frequency switching overhead, neither of which are considered in any of the existing approaches. The classified frequency is applied on the processing cores to execute the workload. The proposed approach is engineered into an embedded multicore system and is validated with a set of standard multimedia applications. Results demonstrate that the proposed approach minimizes energy consumption by an average 20% as compared to the existing techniques.

PDF date15.pdf - Accepted Manuscript
Download (731kB)

More information

e-pub ahead of print date: 9 March 2015
Venue - Dates: Conference on Design, Automation & Test in Europe, France, 2015-03-09 - 2015-03-13
Organisations: Electronic & Software Systems

Identifiers

Local EPrints ID: 370707
URI: http://eprints.soton.ac.uk/id/eprint/370707
PURE UUID: ea2992ad-dd04-4a18-884a-3fcfb9d05ca8
ORCID for Geoff V. Merrett: ORCID iD orcid.org/0000-0003-4980-3894

Catalogue record

Date deposited: 06 Nov 2014 13:04
Last modified: 17 Jul 2017 21:49

Export record

Contributors

Author: Anup K. Das
Author: Rishad Ahmed Shafik
Author: Geoff V. Merrett ORCID iD
Author: B.M. Hashimi
Author: Akash Kumar
Author: Bharadwaj Veeravalli

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×