A Transparent View on Approximate Computing Methods for Tuning Applications

  • Michael BrombergerEmail author
  • Wolfgang Karl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11203)


Approximation-tolerant applications give a system designer the possibility to improve traditional design values by slightly decreasing the quality of result. Approximate computing methods introduced for various system layers present the right tools to exploit this potential. However, finding a suitable tuning for a set of methods during design or run time according to the constraints and the system state is tough. Therefore, this paper presents an approach that leads to a transparent view on different approximation methods. This transparent and abstract view can be exploited by tuning approaches to find suitable parameter settings for the current purpose. Furthermore, the presented approach takes multiple objectives and conventional methods, which influence traditional design values, into account. Besides this novel representation approach, this paper introduces a first tuning approach exploiting the presented approach.


Approximate computing Tuning Abstraction 


  1. 1.
    Baek, W., Chilimbi, T.M.: Green: a framework for supporting energy-conscious programming using controlled approximation. In: ACM SIGPLAN Notices, vol. 45, pp. 198–209. ACM (2010)Google Scholar
  2. 2.
    Bromberger, M., Heuveline, V., Karl, W.: Reducing energy consumption of data transfers using runtime data type conversion. In: Hannig, F., Cardoso, J.M.P., Pionteck, T., Fey, D., Schröder-Preikschat, W., Teich, J. (eds.) ARCS 2016. LNCS, vol. 9637, pp. 239–250. Springer, Cham (2016). Scholar
  3. 3.
    Bromberger, M., Hoffmann, M., Rehrmann, R.: Do iterative solvers benefit from approximate computing? An evaluation study considering orthogonal approximation methods. In: Berekovic, M., Buchty, R., Hamann, H., Koch, D., Pionteck, T. (eds.) ARCS 2018. LNCS, vol. 10793, pp. 297–310. Springer, Cham (2018). Scholar
  4. 4.
    Chippa, V., Chakradhar, S., Roy, K., Raghunathan, A.: Analysis and characterization of inherent application resilience for approximate computing. In: DAC 13: Proceedings of the 50th Annual Design Automation Conference, pp. 113:1–113:9. ACM, New York (2013)Google Scholar
  5. 5.
    Esmaeilzadeh, H., Sampson, A., Ceze, L., Burger, D.: Neural acceleration for general-purpose approximate programs. In: Proceedings of the 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO-45, pp. 449–460. IEEE Computer Society, Washington, DC (2012)Google Scholar
  6. 6.
    Gonzalez, R., Horowitz, M.: Energy dissipation in general purpose microprocessors. IEEE J. Solid State Circ. 31(9), 1277–1284 (1996)CrossRefGoogle Scholar
  7. 7.
    Laurenzano, M.A., Hill, P., Samadi, M., Mahlke, S., Mars, J., Tang, L.: Input responsiveness: using canary inputs to dynamically steer approximation. ACM SIGPLAN Not. 51(6), 161–176 (2016)CrossRefGoogle Scholar
  8. 8.
    Liu, S., Pattabiraman, K., Moscibroda, T., Zorn, B.G.: Flikker: saving dram refresh-power through critical data partitioning. In: Proceedings of the Sixteenth International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS XVI, pp. 213–224. ACM, New York (2011).
  9. 9.
    Samadi, M., Lee, J., Jamshidi, D.A., Hormati, A., Mahlke, S.: Sage: self-tuning approximation for graphics engines. In: Proceedings of the 46th Annual IEEE/ACM International Symposium on Microarchitecture, pp. 13–24. ACM (2013)Google Scholar
  10. 10.
    Sidiroglou-Douskos, S., Misailovic, S., Hoffmann, H., Rinard, M.: Managing performance vs. accuracy trade-offs with loop perforation. In: ESEC/FSE, pp. 124–134. ACM (2011)Google Scholar
  11. 11.
    Sui, X., Lenharth, A., Fussell, D.S., Pingali, K.: Proactive control of approximate programs. SIGOPS Oper. Syst. Rev. 50(2), 607–621 (2016). Scholar
  12. 12.
    Zilberstein, S.: Operational Rationality through Compilation of Anytime Algorithms. Ph.D. thesis, University of California at Berkeley (1993)Google Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Karlsruhe Institute of TechnologyKarlsruheGermany

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