research-article

Enhancing Speculative Execution With Selective Approximate Computing

Authors:

Bernard Nongpoh,

Ansuman BanerjeeAuthors Info & Claims

ACM Transactions on Design Automation of Electronic Systems (TODAES), Volume 24, Issue 2

Article No.: 26, Pages 1 - 29

https://doi.org/10.1145/3307651

Published: 14 February 2019 Publication History

Abstract

Speculative execution is an optimization technique used in modern processors by which predicted instructions are executed in advance with an objective of overlapping the latencies of slow operations. Branch prediction and load value speculation are examples of speculative execution used in modern pipelined processors to avoid execution stalls. However, speculative executions incur a performance penalty as an execution rollback when there is a misprediction. In this work, we propose to aid speculative execution with approximate computing by relaxing the execution rollback penalty associated with a misprediction. We propose a sensitivity analysis method for data and branches in a program to identify the data load and branch instructions that can be executed without any rollback in the pipeline and yet can ensure a certain user-specified quality of service of the application with a probabilistic reliability. Our analysis is based on statistical methods, particularly hypothesis testing and Bayesian analysis. We perform an architectural simulation of our proposed approximate execution and report the benefits in terms of CPU cycles and energy utilization on selected applications from the AxBench, ACCEPT, and Parsec 3.0 benchmarks suite.

References

[1]

Richard C. Aster, Brian Borchers, and Clifford H. Thurber. 2011. Parameter Estimation and Inverse Problems. Vol. 90. Academic Press.

[2]

D. I. August, D. A. Connors, S. A. Mahlke, J. W. Sias, K. M. Crozier, B.-C. Cheng, et al. 1998. Integrated predicated and speculative execution in the IMPACT EPIC architecture. In Proceedings of the 25th Annual International Symposium on Computer Architecture. 227--237.

Digital Library

[3]

Christian Bienia, Sanjeev Kumar, Jaswinder Pal Singh, and Kai Li. 2008. The PARSEC benchmark suite: Characterization and architectural implications. In Proceedings of the 17th International Conference on Parallel Architectures and Compilation Techniques (PACT’08). ACM, New York, NY, 72--81.

Digital Library

[4]

Trevor E. Carlson, Wim Heirman, Stijn Eyerman, Ibrahim Hur, and Lieven Eeckhout. 2014. An evaluation of high-level mechanistic core models. ACM Transactions on Architecture and Code Optimization 11, 3 (Oct. 2014), Article 5, 23 pages.

Digital Library

[5]

I.-Cheng K. Chen, C.-C. Lee, and T. N. Mudge. 1997. Instruction prefetching using branch prediction information. In Proceedings of the International Conference on Computer Design VLSI in Computers and Processors (ICCD’97). 593--601.

Digital Library

[6]

P. Düben, Parishkrati, S. Yenugula, J. Augustine, K. Palem, J. Schlachter, C. Enz, et al. 2015. Opportunities for energy efficient computing: A study of inexact general purpose processors for high-performance and big-data applications. In Proceedings of the 2015 Design, Automation, and Test in Europe Conference and Exhibition (DATE’15). 764--769.

Digital Library

[7]

A. N. Eden et al. 1998. The YAGS branch prediction scheme. In Proceedings of the 31st Annual ACM/IEEE International Symposium on Microarchitecture (MICRO’98). 69--77.

Digital Library

[8]

Hadi Esmaeilzadeh, Adrian Sampson, Luis Ceze, and Doug Burger. 2012. Neural acceleration for general-purpose approximate programs. In Proceedings of the 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO-45). IEEE, Washington, DC, 449--460.

Digital Library

[9]

Hadi Esmaeilzadeh, Adrian Sampson, Luis Ceze, and Doug Burger. 2012. Neural acceleration for general-purpose approximate programs. In Proceedings of the 45th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO’12). IEEE, Los Alamitos, CA, 449--460.

Digital Library

[10]

William Feller. 1968. An Introduction to Probability Theory and Its Applications. Vol. 1. Wiley. http://www.amazon.ca/exec/obidos/redirect?tag=citeulike04-20&path===ASIN/0471257087.

[11]

José González and Antonio González. 1997. Speculative execution via address prediction and data prefetching. In Proceedings of the 11th International Conference on Supercomputing (ICS’97). 196--203.

Digital Library

[12]

J. Han and M. Orshansky. 2013. Approximate computing: An emerging paradigm for energy-efficient design. In Proceedings of the 2013 18th IEEE European Test Symposium (ETS’13). 1--6.

[13]

X. He, S. Jiang, W. Lu, G. Yan, Y. Han, and X. Li. 2017. Exploiting the potential of computation reuse through approximate computing. IEEE Transactions on Multi-Scale Computing Systems 3, 3 (July 2017), 152--165.

[14]

John L. Hennessy and David A. Patterson. 2011. Computer Architecture: A Quantitative Approach. Morgan Kaufmann.

Digital Library

[15]

N. Kanopoulos, N. Vasanthavada, and R. L. Baker. 1988. Design of an image edge detection filter using the Sobel operator. IEEE Journal of Solid-State Circuits 23, 2 (April 1988), 358--367.

[16]

Thomas Lengauer and Robert Endre Tarjan. 1979. A fast algorithm for finding dominators in a flowgraph. ACM Transactions on Programming Languages and Systems 1, 1 (Jan. 1979), 121--141.

Digital Library

[17]

Sheng Li, Jung Ho Ahn, Richard D. Strong, Jay B. Brockman, Dean M. Tullsen, and Norman P. Jouppi. 2009. McPAT: An integrated power, area, and timing modeling framework for multicore and manycore architectures. In Proceedings of the 2009 42nd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO-42). 469--480.

Digital Library

[18]

Mikko H. Lipasti, Christopher B. Wilkerson, and John Paul Shen. 1996. Value locality and load value prediction. ACM SIGPLAN Notices 31, 9 (1996), 138--147.

Digital Library

[19]

Chi-Keung Luk, Robert Cohn, Robert Muth, Harish Patil, Artur Klauser, Geoff Lowney, et al. 2005. Pin: Building customized program analysis tools with dynamic instrumentation. In Proceedings of the 2005 ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI’05). ACM, New York, NY, 190--200.

Digital Library

[20]

Avi Mendelson and Freddy Gabbay. 1996. Speculative Execution Based on Value Prediction. Technical Report. EE Department TR 1080, Technion—Israel Institute of Technology.

[21]

Joshua San Miguel, Mario Badr, and Natalie Enright Jerger. 2014. Load value approximation. In Proceedings of the 47th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO’14). IEEEE, Washington, DC, 127--139.

Digital Library

[22]

Sparsh Mittal. 2016. A survey of techniques for approximate computing. ACM Computing Surveys 48, 4, Article 62, 33 pages.

Digital Library

[23]

B. Nongpoh, R. Ray, S. Dutta, and A. Banerjee. 2017. AutoSense: A framework for automated sensitivity analysis of program data. IEEE Transactions of Software Engineering 43, 12 (2017), 1110--1124.

Digital Library

[24]

Gennady Pekhimenko, Danai Koutra, and Kun Qian. 2011. Approximate computing: Application analysis and hardware design. Retrieved from ww.cs.cmu.edu/&sim;gpekhime/Projects/15740/paper.pdf.

[25]

B. Ramakrishna Rau and J. A. Fisher. 1993. Instruction-level parallel processing: History, overview, and perspective. Journal of Supercomputing 7, 1–2 (1993), 9--50.

Digital Library

[26]

Martin Rinard. 2006. Probabilistic accuracy bounds for fault-tolerant computations that discard tasks. In Proceedings of the 20th Annual International Conference on Supercomputing (ICS’06). ACM, New York, NY, 324--334.

Digital Library

[27]

Vijay K. Rohatgi and A. K. Md. Ehsanes Saleh. 2015. An Introduction to Probability and Statistics. John Wiley 8 Sons.

[28]

Pooja Roy, Rajarshi Ray, Chundong Wang, and Weng Fai Wong. 2014. ASAC: Automatic sensitivity analysis for approximate computing. ACM SIGPLAN Notices 49, 5 (June 2014), 95--104.

Digital Library

[29]

Reuven Y. Rubinstein and Dirk P. Kroese. 2007. Simulation and the Monte Carlo Method (2nd ed.). Wiley Series in Probability and Statistics. Wiley.

Digital Library

[30]

Stuart J. Russell and Peter Norvig. 2003. Artificial Intelligence: A Modern Approach (2nd ed.). Pearson Education.

Digital Library

[31]

Adrian Sampson, Andre Baixo, Benjamin Ransford, Thierry Moreau, Joshua Yip, Luis Ceze, et al. 2015. ACCEPT: A Programmer-Guided Compiler Framework for Practical Approximate Computing. Technical Report UW-CSE-15-01-1. University of Washington.

[32]

Adrian Sampson, Werner Dietl, Emily Fortuna, Danushen Gnanapragasam, Luis Ceze, and Dan Grossman. 2011. EnerJ: Approximate data types for safe and general low-power computation. ACM SIGPLAN Notices 46, 6 (June 2011), 164--174.

Digital Library

[33]

N. Nethercote and J. Seward. 2007. Valgrind: A framework for heavyweight dynamic binary instrumentation. SIGPLAN Not. 42, 6 (2007), 89--100.

Digital Library

[34]

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

Digital Library

[35]

Bradley Thwaites, Gennady Pekhimenko, Hadi Esmaeilzadeh, Amir Yazdanbakhsh, Onur Mutlu, Jongse Park, et al. 2014. Rollback-free value prediction with approximate loads. In Proceedings of the 23rd International Conference on Parallel Architectures and Compilation (PACT’14). ACM, New York, NY, 493--494.

Digital Library

[36]

G. S. Tyson. 1994. The effects of predicated execution on branch prediction. In Proceedings of the 26th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO’94). 196--206.

Digital Library

[37]

Vassilis Vassiliadis, Jan Riehme, Jens Deussen, Konstantinos Parasyris, Christos D. Antonopoulos, Nikolaos Bellas, et al. 2016. Towards automatic significance analysis for approximate computing. In Proceedings of the 2016 International Symposium on Code Generation and Optimization (CGO’16). 182--193.

Digital Library

[38]

Swagath Venkataramani, Srimat T. Chakradhar, Kaushik Roy, and Anand Raghunathan, 2015. Approximate computing and the quest for computing efficiency. In Proceedings of the 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC’15). Article 120, 6 pages.

Digital Library

[39]

A. Wald. 1945. Sequential tests of statistical hypotheses. Annals of Mathematical Statistics 16, 2 (June 1945), 117--186.

[40]

N. Wang, M. Fertig, and S. Patel. 2003. Y-branches: When you come to a fork in the road, take it. In Proceedings of the 2003 12th International Conference on Parallel Architectures and Compilation Techniques (PACT’03). 56--66.

Digital Library

[41]

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. 2004. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13, 4 (April 2004), 600--612.

Digital Library

[42]

William A. Wulf and Sally A. McKee. 1995. Hitting the memory wall: Implications of the obvious. ACM SIGARCH Computer Architecture News 23, 1 (March 1995), 20--24.

Digital Library

[43]

H. Wunderlich, C. Braun, and A. Scholl. 2016. Pushing the limits: How fault tolerance extends the scope of approximate computing. In Proceedings of the 2016 IEEE 22nd International Symposium on On-Line Testing and Robust System Design (IOLTS’16). 133--136.

[44]

Amir Yazdanbakhsh, Divya Mahajan, Bradley Thwaites, Jongse Park, Anandhavel Nagendrakumar, Sindhuja Sethuraman, et al. 2015. Axilog: Language support for approximate hardware design. In Proceedings of the 2015 Design, Automation, and Test in Europe Conference and Exhibition (DATE’15). 812--817.

Digital Library

[45]

Amir Yazdanbakhsh, Gennady Pekhimenko, Bradley Thwaites, Hadi Esmaeilzadeh, Onur Mutlu, and Todd C. Mowry. 2016. RFVP: Rollback-free value prediction with safe-to-approximate loads. ACM Transactions on Architecture and Code Optimization 12, 4 (Jan. 2016), Article 62, 26 pages.

Digital Library

[46]

Hakan L. S. Younes and Reid G. Simmons. 2002. Probabilistic verification of discrete event systems using acceptance sampling. In Computer Aided Verification. Lecture Notes in Computer Science, Vol. 2404. Springer, 223--235.

Digital Library

Cited By

Damsgaard HGrenier AKatare DTaufique ZShakibhamedan STroccoli TChatzitsompanis GKanduri AOmetov ADing ATaherinejad NKarakonstantis GWoods RNurmi J(2024)Adaptive approximate computing in edge AI and IoT applications: A reviewJournal of Systems Architecture10.1016/j.sysarc.2024.103114150(103114)Online publication date: May-2024
https://doi.org/10.1016/j.sysarc.2024.103114
Damsgaard HOmetov ANurmi J(2023)Approximation Opportunities in Edge Computing Hardware: A Systematic Literature ReviewACM Computing Surveys10.1145/357277255:12(1-49)Online publication date: 3-Mar-2023
https://dl.acm.org/doi/10.1145/3572772
Kamahori KTakamaeda-Yamazaki S(2022)Accelerating Decision Tree Ensemble with Guided Branch ApproximationProceedings of the 12th International Symposium on Highly-Efficient Accelerators and Reconfigurable Technologies10.1145/3535044.3535048(24-32)Online publication date: 9-Jun-2022
https://dl.acm.org/doi/10.1145/3535044.3535048
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Index Terms

Enhancing Speculative Execution With Selective Approximate Computing
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Processors and memory architectures
    2. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems

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Published In

cover image ACM Transactions on Design Automation of Electronic Systems

ACM Transactions on Design Automation of Electronic Systems Volume 24, Issue 2

March 2019

287 pages

ISSN:1084-4309

EISSN:1557-7309

DOI:10.1145/3306156

Editor:
Naehyuck Chang
Korea Advanced Institute of Science and Technology, Korea

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Copyright © 2019 ACM.

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 14 February 2019

Accepted: 01 January 2019

Revised: 01 November 2018

Received: 01 April 2018

Published in TODAES Volume 24, Issue 2

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National Institute of Technology Meghalaya and Visvesvaraya Ph.D. Scheme, Government of India

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Cited By

Damsgaard HGrenier AKatare DTaufique ZShakibhamedan STroccoli TChatzitsompanis GKanduri AOmetov ADing ATaherinejad NKarakonstantis GWoods RNurmi J(2024)Adaptive approximate computing in edge AI and IoT applications: A reviewJournal of Systems Architecture10.1016/j.sysarc.2024.103114150(103114)Online publication date: May-2024
https://doi.org/10.1016/j.sysarc.2024.103114
Damsgaard HOmetov ANurmi J(2023)Approximation Opportunities in Edge Computing Hardware: A Systematic Literature ReviewACM Computing Surveys10.1145/357277255:12(1-49)Online publication date: 3-Mar-2023
https://dl.acm.org/doi/10.1145/3572772
Kamahori KTakamaeda-Yamazaki S(2022)Accelerating Decision Tree Ensemble with Guided Branch ApproximationProceedings of the 12th International Symposium on Highly-Efficient Accelerators and Reconfigurable Technologies10.1145/3535044.3535048(24-32)Online publication date: 9-Jun-2022
https://dl.acm.org/doi/10.1145/3535044.3535048
Pomeranz I(2022)Usable Circuits with Imperfect Scan Logic2022 IEEE 31st Asian Test Symposium (ATS)10.1109/ATS56056.2022.00039(156-161)Online publication date: Nov-2022
https://doi.org/10.1109/ATS56056.2022.00039
Nongpoh BRay RBanerjee ARoop PZhan NGao SNuzzo P(2019)Approximate computing for multithreaded programs in shared memory architecturesProceedings of the 17th ACM-IEEE International Conference on Formal Methods and Models for System Design10.1145/3359986.3361209(1-9)Online publication date: 9-Oct-2019
https://dl.acm.org/doi/10.1145/3359986.3361209

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