research-article

Managing the Quality vs. Efficiency Trade-off Using Dynamic Effort Scaling

Authors:

Vinay K. Chippa,

Srimat T. Chakradhar,

Anand RaghunathanAuthors Info & Claims

ACM Transactions on Embedded Computing Systems (TECS), Volume 12, Issue 2s

Article No.: 90, Pages 1 - 23

https://doi.org/10.1145/2465787.2465792

Published: 01 May 2013 Publication History

Abstract

Several current and emerging applications do not have a unique result for a given input; rather, functional correctness is defined in terms of output quality. Recently proposed design techniques exploit the inherent resilience of such applications and achieve improved efficiency (energy or performance) by foregoing correct execution of all the constituent computations. Hardware and software systems that are thus designed may be viewed as scalable effort systems, since they offer the capability to modulate the effort that they expend towards computation, thereby allowing for trade-offs between output quality and efficiency.

We propose the concept of Dynamic Effort Scaling (DES), which refers to dynamic management of the control knobs that are exposed by scalable effort systems. We argue the need for DES by observing that the degree of resilience often varies significantly across applications, across datasets, and even within a dataset. We propose a general conceptual framework for DES by formulating it as a feedback control problem, wherein the scaling mechanisms are regulated with the goal of maintaining output quality at or above a specified limit. We present an implementation of Dynamic Effort Scaling for recognition and mining applications and evaluate it for the support vector machines and K-means clustering algorithms under various application scenarios and datasets. Our results clearly demonstrate the benefits of the proposed approach---statically setting the scaling mechanisms leads to either significant error overshoot or significant opportunities for energy savings left on the table unexploited. In contrast, DES is able to effectively regulate the output quality while maximally exploiting the time-varying resiliency in the workload.

References

[1]

Astrom, K. and Hagglund, T., Eds. 1995. PID Controllers: Theory, Design, and Tuning 2nd Ed. ISA, Raleigh, NC.

[2]

Baek, W. and Chilimbi, T. M. 2010. Green: A framework for supporting energy-conscious programming using controlled approximation. In Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI’10). 198--209.

Digital Library

[3]

Benini, L. and DeMicheli, G. 1998. Dynamic Power Management: Design Techniques and CAD Tools. Kluwer Academic Publishers, Norwell, MA.

Digital Library

[4]

Breuer, M. A. 2010. Hardware that produces bounded rather than exact results. In Proceedings of the 47th Design Automation Conference (DAC’10). 871--876.

Digital Library

[5]

Chandrakasan, A. and Brodersen, R., Eds. 1997. Low-Power CMOS Design 1st Ed. Wiley-IEEE Press, Hoboken, NY.

Digital Library

[6]

Chakradhar, S. T. and Raghunathan, A. 2010. Best-effort computing: Re-thinking parallel software and hardware. In Proceedings of the 47th Design Automation Conference (DAC’10). 865--870.

Digital Library

[7]

Chippa, V. K., Mohapatra, D., A.Raghunathan, Roy, K., and Chakradhar, S. 2010. Scalable effort hardware design: Exploiting algorithmic resilience for energy efficiency. In Proceedings of the 47th Design Automation Conference (DAC’10). 555--560.

Digital Library

[8]

Ernst, D., Kim, N. S., Das, S., Pant, S., Pham, T., Rao, R., Ziesler, C., Blaauw, D., Austin, T., Mudge, T., and Flautner, K. 2003. Razor: A low-power pipeline based on circuit-level timing speculation. In Proceedings of the 36th International Symposium on Microarchitecture (MICRO’03). 7--18.

Digital Library

[9]

Ghosh, S., Bhunia, S., and Roy, K. 2007. CRISTA: A new paradigm for low-power, variation-tolerant, and adaptive circuit synthesis using critical path isolation. IEEE Trans. CAD Integr. Circuits Syst. 26, 11, 1947--1956.

Digital Library

[10]

Hegde, R. and Shanbhag, N. R. 1999. Energy-efficient signal processing via algorithmic noise-tolerance. In Proceedings of the International Symposium on Low Power Electronics and Design (ISLPED’99). 30--35.

Digital Library

[11]

Hegde, R. and Shanbhag, N. R. 2001. A low-power digital filter ic via soft dsp. In Proceedings of the 23rd Custom Integrated Circuits Conference (CICC’01). 309--312.

[12]

Hoffmann, H., Sidiroglou, S., Carbin, M., Misailovic, S., Agarwal, A., and Rinard, M. C. 2011. Dynamic knobs for responsive power-aware computing. In Proceedings of the 16th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2011). 199--212.

Digital Library

[13]

Hsieh, T.-Y., Lee, K.-J., and Breuer, M. A. 2008. An error rate based test methodology to support error-tolerance. IEEE Trans. Reliab. 57, 1, 204--214.

[14]

Ivanov, Y. and Bleakley, C. 2007. Dynamic complexity scaling for real-time H.264/AVC video encoding. In Proceedings of the 15th International Conference on Multimedia (MULTIMEDIA’07). 962--970.

Digital Library

[15]

Jiang, Z. and Gupta, S. K. 2002. An ATPG for threshold testing: Obtaining acceptable yield in future processes. In Proceedings of the IEEE International Test Conference (ITC’02). 824--833.

Digital Library

[16]

Leem, L., Cho, H., Bau, J., Jacobson, Q. A., and Mitra, S. 2010. ERSA: Error resilient system architecture for probabilistic applications. In Proceedings of Design, Automation and Test in Europe (DATE’10). 1560--1565.

Digital Library

[17]

Martin, D., Fowlkes, C., Tal, D., and Malik, J. 2001. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proceedings of the 8th International Conference on Computer Vision. 416--423.

[18]

Meng, J., Chakradhar, S. T., and Raghunathan, A. 2009. Best-effort parallel execution framework for recognition and mining applications. In Proceedings of the 23rd IEEE International Symposium on Parallel and Distributed Systems (IPDPS’09). 1--12.

Digital Library

[19]

Meng, J., Sheaffer, J. W., and Skadron, K. 2010. Exploiting inter-thread temporal locality for chip multithreading. In Proceedings of the 24th IEEE International Symposium on Parallel and Distributed Systems (IPDPS’10). 1--12.

[20]

Mohapatra, D., Karakonstantis, G., and Roy, K. 2009. Significance driven computation: A voltage-scalable, variation-aware, quality-tuning motion estimator. In Proceedings of the 15th International Symposium on Low Power Electronics and Design (ISLPED’09). 195--200.

Digital Library

[21]

Mohapatra, D., Chippa, V. K., Raghunathan, A., and Roy, K. 2011. Design of voltage scalable metafunctions for multimedia, recoginition and mining applications. In Proceedings of Design, Automation and Test Europe (DATE’11). 950--955.

[22]

Palem, K. V. 2003. Energy aware algorithm design via probabilistic computing: From algorithms and models to moore’s law and novel (semiconductor) devices. In Proceedings of the International Conference on Compilers, Architecture and Synthesis for Embedded Systems (CASES’03). 113--116.

Digital Library

[23]

Palem, K. V., Chakrapani, L. N., Kedem, Z. M., Lingamneni, A., and Muntimadugu, K. K. 2009. Sustaining Moore’s Law in embedded computing through probabilistic and approximate design: Retrospects and prospects. In Proceedings of the International Conference on Compilers, Architecture and Synthesis for Embedded Systems (CASES’09). 11--16.

Digital Library

[24]

Park, J., Shin, D., Chang, N., and Pedram, M. 2010. Accurate modeling and calculation of delay and energy overheads of dynamic voltage scaling in modern high-performance microprocessors. In Proceedings of the 16th International Symposium on Low Power Electronics and Design (ISLPED’10). 419--424.

Digital Library

[25]

Shafique., M., Bauer, L., and Henkel, J. 2010. enBudget: A run-time adaptive predictive energy-budgeting scheme for energy-aware motion estimation in H.264/MPEG-4 AVC video encoder. In Proceedings of Design, Automation and Test in Europe (DATE’10). 1725--1730.

Digital Library

[26]

Shanbhag, N. R. 2002. Reliable and energy-efficient digital signal processing. In Proceedings of the 39th Design Automation Conference (DAC’02). 830--835.

Digital Library

[27]

Shanbhag, N. R., Abdallah, R. A., Kumar, R., and Jones, D. L. 2010. Stochastic computation. In Proceedings of the 47th Design Automation Conference (DAC’10). 859--864.

Digital Library

[28]

Sidiroglou-Douskos, S., Misailovic, S., Hoffmann, H., and Rinard, M. C. 2011. Managing performance vs. accuracy trade-offs with loop perforation. In Proceedings of the 19th ACM SIGSOFT Symposium on the Foundations of Software Engineering (SIGSOFT/FSE’11) and 13th European Software Engineering Conference (ESEC’11). 124--134.

Digital Library

[29]

Vapnik, V. 1995. The Nature of Statistical Learning Theory. Springer-Verlag New York, Inc., New York, NY.

Digital Library

[30]

Varatkar, G. V. and Shanbhag, N. R. 2008. Error-resilient motion estimation architecture. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 16, 10, 1399--1412.

Digital Library

[31]

Wong, V. and Horowitz, M. 2006. Soft error resilience of probabilistic inference applications. In Proceedings of the 2nd Workshop on System Effects of Logic Soft Errors (SELSE’06).

[32]

Yoo, R. M., Romano, A., and Kozyrakis, C. 2009. Phoenix rebirth: Scalable MapReduce on a large-scale shared-memory system. In Proceedings of the IEEE International Symposium on Workload Characterization (IISWC’09). 198--207.

Digital Library

Cited By

Yu HHa YVeeravalli BChen FEl-Sayed H(2021)DVFS-Based Quality Maximization for Adaptive Applications With Diminishing ReturnIEEE Transactions on Computers10.1109/TC.2020.299724270:5(803-816)Online publication date: 1-May-2021
https://doi.org/10.1109/TC.2020.2997242
Chen FYu HHa Y(2020)Quality Estimation and Optimization of Adaptive Stereo Matching Algorithms for Smart VehiclesACM Transactions on Embedded Computing Systems10.1145/337278419:2(1-24)Online publication date: 10-Feb-2020
https://dl.acm.org/doi/10.1145/3372784
Zhang QXu Q(2020)ApproxIt: A Quality Management Framework of Approximate Computing for Iterative MethodsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2017.277523639:5(991-1002)Online publication date: May-2020
https://doi.org/10.1109/TCAD.2017.2775236
Show More Cited By

Index Terms

Managing the Quality vs. Efficiency Trade-off Using Dynamic Effort Scaling
1. Hardware
  1. Integrated circuits

Recommendations

Dynamic effort scaling: managing the quality-efficiency tradeoff
DAC '11: Proceedings of the 48th Design Automation Conference

Several recently proposed design techniques leverage the inherent error resilience of applications for improved efficiency (energy or performance). Hardware and software systems that are thus designed may be viewed as "scalable effort systems", since ...
Scalable effort hardware design: exploiting algorithmic resilience for energy efficiency
DAC '10: Proceedings of the 47th Design Automation Conference

Algorithms from several interesting application domains exhibit the property of inherent resilience to "errors" from extrinsic or intrinsic sources, offering entirely new avenues for performance and power optimization by relaxing the conventional ...
Neural acceleration for GPU throughput processors
MICRO-48: Proceedings of the 48th International Symposium on Microarchitecture

Graphics Processing Units (GPUs) can accelerate diverse classes of applications, such as recognition, gaming, data analytics, weather prediction, and multimedia. Many of these applications are amenable to approximate execution. This application ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Embedded Computing Systems

ACM Transactions on Embedded Computing Systems Volume 12, Issue 2s

Special Section on Probabilistic Embedded Computing

May 2013

269 pages

ISSN:1539-9087

EISSN:1558-3465

DOI:10.1145/2465787

Issue’s Table of Contents

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 01 May 2013

Accepted: 01 November 2011

Revised: 01 September 2011

Received: 01 June 2011

Published in TECS Volume 12, Issue 2s

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

National Science Foundation

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
439
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)3

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Yu HHa YVeeravalli BChen FEl-Sayed H(2021)DVFS-Based Quality Maximization for Adaptive Applications With Diminishing ReturnIEEE Transactions on Computers10.1109/TC.2020.299724270:5(803-816)Online publication date: 1-May-2021
https://doi.org/10.1109/TC.2020.2997242
Chen FYu HHa Y(2020)Quality Estimation and Optimization of Adaptive Stereo Matching Algorithms for Smart VehiclesACM Transactions on Embedded Computing Systems10.1145/337278419:2(1-24)Online publication date: 10-Feb-2020
https://dl.acm.org/doi/10.1145/3372784
Zhang QXu Q(2020)ApproxIt: A Quality Management Framework of Approximate Computing for Iterative MethodsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2017.277523639:5(991-1002)Online publication date: May-2020
https://doi.org/10.1109/TCAD.2017.2775236
Javed RSiddique AHafiz RHasan OShafique M(2018)ApproxCT: Approximate Clustering Techniques for Energy Efficient Computer Vision in Cyber-Physical Systems2018 12th International Conference on Open Source Systems and Technologies (ICOSST)10.1109/ICOSST.2018.8632191(64-70)Online publication date: Dec-2018
https://doi.org/10.1109/ICOSST.2018.8632191
Yu HHa YWang JGiorgi RBecchi MPalumbo F(2017)Quality Optimization of Resilient Applications under Temperature ConstraintsProceedings of the Computing Frontiers Conference10.1145/3075564.3075577(9-16)Online publication date: 15-May-2017
https://dl.acm.org/doi/10.1145/3075564.3075577
Acharya MPal CMaity SChakrabarti A(2017)Inexact Implementation of Wavelet Transform and Its Performance Evaluation Through Bit Width ReductionAdvances in Computing Applications10.1007/978-981-10-2630-0_14(227-242)Online publication date: 20-Jan-2017
https://doi.org/10.1007/978-981-10-2630-0_14
Zhang QYuan FYe RXu Q(2014)ApproxItProceedings of the 51st Annual Design Automation Conference10.1145/2593069.2593092(1-6)Online publication date: 1-Jun-2014
https://dl.acm.org/doi/10.1145/2593069.2593092
Qian Zhang Yuan FYe RXu Q(2014)ApproxIt: An approximate computing framework for iterative methods2014 51st ACM/EDAC/IEEE Design Automation Conference (DAC)10.1109/DAC.2014.6881424(1-6)Online publication date: Jun-2014
https://doi.org/10.1109/DAC.2014.6881424
Chippa VVenkataramani SChakradhar SRoy KRaghunathan A(2013)Approximate computing: An integrated hardware approach2013 Asilomar Conference on Signals, Systems and Computers10.1109/ACSSC.2013.6810241(111-117)Online publication date: Nov-2013
https://doi.org/10.1109/ACSSC.2013.6810241

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 Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents