research-article

Exploiting processor heterogeneity for energy efficient context inference on mobile phones

Authors:

Chenguang Shen,

Supriyo Chakraborty,

Kasturi Rangan Raghavan,

Mani B. SrivastavaAuthors Info & Claims

HotPower '13: Proceedings of the Workshop on Power-Aware Computing and Systems

Article No.: 9, Pages 1 - 5

https://doi.org/10.1145/2525526.2525856

Published: 03 November 2013 Publication History

Abstract

In recent years we have seen the emergence of context-aware mobile sensing apps which employ machine learning algorithms on real-time sensor data to infer user behaviors and contexts. These apps are typically optimized for power and performance on the app processors of mobile platforms. However, modern mobile platforms are sophisticated system on chips (SoCs) where the main app processors are complemented by multiple co-processors. Recently chip vendors have undertaken nascent efforts to make these previously hidden co-processors such as the digital signal processors (DSPs) programmable. In this paper, we explore the energy and performance implications of off-loading the computation associated with machine learning algorithms in context-aware apps to DSPs embedded in mobile SoCs. Our results show a 17% reduction in a TI OMAP4 based mobile platform's energy usage from off-loading context classification computation to the DSP core with indiscernible latency overhead. We also describe the design of a run-time system service for energy efficient context inference on Android devices, which takes parameters from the app to instantiate the classification model and schedules the execution on the DSP or app processor as specified by the app.

References

[1]

Apple iPhone5s. http://goo.gl/1LUSj0.

[2]

Google Activity Recognition API. http://goo.gl/mYJn84.

[3]

Qualcomm Snapdragon. http://goo.gl/ZFTm0.

[4]

Renderscript. http://goo.gl/W1jGz.

[5]

TI OMAP. http://goo.gl/9Z5R4.

[6]

TI Pandaboard. http://goo.gl/ujdiL.

[7]

VOICEBOX: Speech Processing Toolbox for MATLAB. http://goo.gl/wakDY.

[8]

C.-C. Chang and C.-J. Lin. Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3): 27, 2011.

Digital Library

[9]

D. Chu, N. D. Lane, T. T.-T. Lai, C. Pang, X. Meng, Q. Guo, F. Li, and F. Zhao. Balancing energy, latency and accuracy for mobile sensor data classification. In Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems (SenSys'11), pages 54--67. ACM, 2011.

Digital Library

[10]

P. Greenhalgh. Big. little processing with arm cortex-a15 & cortex-a7. ARM White Paper, 2011.

[11]

T. Huynh, M. Fritz, and B. Schiele. Discovery of activity patterns using topic models. In Proceedings of the 10th international conference on Ubiquitous computing (UbiComp'08), pages 10--19. ACM, 2008.

Digital Library

[12]

Y. Ju, Y. Lee, J. Yu, C. Min, I. Shin, and J. Song. Symphoney: a coordinated sensing flow execution engine for concurrent mobile sensing applications. In Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems (SenSys'12), pages 211--224. ACM, 2012.

Digital Library

[13]

F. X. Lin, Z. Wang, R. LiKamWa, and L. Zhong. Reflex: using low-power processors in smartphones without knowing them. In Proceedings of the ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS XVII),. ACM, March 2012.

Digital Library

[14]

F. X. Lin, Z. Wang, and L. Zhong. Supporting distributed execution of smartphone workloads on loosely coupled heterogeneous processors. In Proceedings of the 4th Workshop on Power-Aware Computing and Systems (HotPower'12), October 2012.

Digital Library

[15]

H. Lu, J. Yang, Z. Liu, N. D. Lane, T. Choudhury, and A. T. Campbell. The jigsaw continuous sensing engine for mobile phone applications. In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems (SenSys'10), pages 71--84. ACM, 2010.

Digital Library

[16]

B. Priyantha, D. Lymberopoulos, and J. Liu. Littlerock: Enabling energy-efficient continuous sensing on mobile phones. Pervasive Computing, IEEE, 10(2): 12--15, 2011.

Digital Library

[17]

M.-R. Ra, B. Priyantha, A. Kansal, and J. Liu. Improving energy efficiency of personal sensing applications with heterogeneous multi-processors. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing (UbiComp'12), pages 1--10. ACM, 2012.

Digital Library

Cited By

Shen CHo BSrivastava M(2018)MiLift: Efficient Smartwatch-Based Workout Tracking Using Automatic SegmentationIEEE Transactions on Mobile Computing10.1109/TMC.2017.277564117:7(1609-1622)Online publication date: 1-Jul-2018
https://doi.org/10.1109/TMC.2017.2775641
Yoo S(2018)Leveraging multicores for mobile edge computing2018 International Conference on Information Networking (ICOIN)10.1109/ICOIN.2018.8343246(869-874)Online publication date: Jan-2018
https://doi.org/10.1109/ICOIN.2018.8343246
Hossain SHnat TSaleheen NNasrin NNoor JHo BCondie TSrivastava MKumar S(2017)mCerebrumProceedings of the 15th ACM Conference on Embedded Network Sensor Systems10.1145/3131672.3131694(1-14)Online publication date: 6-Nov-2017
https://dl.acm.org/doi/10.1145/3131672.3131694
Show More Cited By

Index Terms

Exploiting processor heterogeneity for energy efficient context inference on mobile phones
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Special purpose systems
2. Software and its engineering
  1. Software notations and tools
    1. Software libraries and repositories

Recommendations

A unified processor architecture for RISC & VLIW DSP
GLSVLSI '05: Proceedings of the 15th ACM Great Lakes symposium on VLSI

This paper presents a unified processor core with two operation modes. The processor core works as a compiler-friendly MIPS-like core in the RISC mode, and it is a 4-way VLIW in its DSP mode, which has distributed and ping-pong register organization ...
Exploiting Processor Workload Heterogeneity for Reducing Energy Consumption in Chip Multiprocessors
DATE '04: Proceedings of the conference on Design, automation and test in Europe - Volume 2

Advances in semiconductor technology are enabling designs with several hundred million transistors. Since building sophisticated single processor based systems is a complex process from design, veri.cation, and software development perspectives, the use ...
Asynchronous DSP for low-power energy-efficient embedded systems

This paper presents the design of an asynchronous DSP that is code compatible with the Motorola DSP56000, with the objective of low power and high energy efficiency to extend the lifespan of the batteries in embedded systems embodying the DSP. It ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

HotPower '13: Proceedings of the Workshop on Power-Aware Computing and Systems

November 2013

66 pages

ISBN:9781450324588

DOI:10.1145/2525526

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGOPS: ACM Special Interest Group on Operating Systems

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 November 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Science Foundation

Conference

SOSP '13

Sponsor:

SIGOPS

SOSP '13: ACM SIGOPS 24th Symposium on Operating Systems Principles

November 3 - 6, 2013

Pennsylvania, Farmington

Acceptance Rates

HotPower '13 Paper Acceptance Rate 13 of 38 submissions, 34%;

Overall Acceptance Rate 20 of 50 submissions, 40%

Upcoming Conference

SOSP '25

Sponsor:
sigops

ACM SIGOPS 31st Symposium on Operating Systems Principles

October 13 - 16, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

20
Total Citations
View Citations
410
Total Downloads

Downloads (Last 12 months)8
Downloads (Last 6 weeks)0

Reflects downloads up to 13 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Shen CHo BSrivastava M(2018)MiLift: Efficient Smartwatch-Based Workout Tracking Using Automatic SegmentationIEEE Transactions on Mobile Computing10.1109/TMC.2017.277564117:7(1609-1622)Online publication date: 1-Jul-2018
https://doi.org/10.1109/TMC.2017.2775641
Yoo S(2018)Leveraging multicores for mobile edge computing2018 International Conference on Information Networking (ICOIN)10.1109/ICOIN.2018.8343246(869-874)Online publication date: Jan-2018
https://doi.org/10.1109/ICOIN.2018.8343246
Hossain SHnat TSaleheen NNasrin NNoor JHo BCondie TSrivastava MKumar S(2017)mCerebrumProceedings of the 15th ACM Conference on Embedded Network Sensor Systems10.1145/3131672.3131694(1-14)Online publication date: 6-Nov-2017
https://dl.acm.org/doi/10.1145/3131672.3131694
Georgiev PLane NMascolo CChu DChoudhury TKo SCampbell AGanesan D(2017)Accelerating Mobile Audio Sensing Algorithms through On-Chip GPU OffloadingProceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services10.1145/3081333.3081358(306-318)Online publication date: 16-Jun-2017
https://dl.acm.org/doi/10.1145/3081333.3081358
He LMeng GGu YLiu CSun JZhu TLiu YShin K(2017)Battery-Aware Mobile Data ServiceIEEE Transactions on Mobile Computing10.1109/TMC.2016.259784216:6(1544-1558)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1109/TMC.2016.2597842
Yao TTsai TChen YChen JChen D(2017)Online energy-efficient real-time task scheduling for heterogeneous multicore systems2017 IEEE 23rd International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA)10.1109/RTCSA.2017.8046317(1-10)Online publication date: Aug-2017
https://doi.org/10.1109/RTCSA.2017.8046317
Snyder TByrd G(2017)The Internet of EverythingComputer10.1109/MC.2017.17950:6(8-9)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1109/MC.2017.179
Shen CSrivastava M(2017)Exploring Hardware Heterogeneity to Improve Pervasive Context InferencesComputer10.1109/MC.2017.17450:6(19-26)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1109/MC.2017.174
Wolf MMukhopadhyay S(2017)VLSI for the Internet of ThingsComputer10.1109/MC.2017.15850:6(16-18)Online publication date: 9-Jun-2017
https://dl.acm.org/doi/10.1109/MC.2017.158
Barik RFarooqui NLewis BHu CShpeisman TFranke BWu YRastello F(2016)A black-box approach to energy-aware scheduling on integrated CPU-GPU systemsProceedings of the 2016 International Symposium on Code Generation and Optimization10.1145/2854038.2854052(70-81)Online publication date: 29-Feb-2016
https://dl.acm.org/doi/10.1145/2854038.2854052
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten