Approximate or Soft Computing


Many programs compute on approximate and/or probabilistic data that are highly tolerant of error. Such computations can tolerate significant corruption and still produce meaningful and useful results. Multimedia computations exhibit such properties. In addition, artificial intelligence computations, an increasingly important application domain, also exhibit such properties. In this project, we characterize the error resilience properties of these computations, and investigate architectural techniques for exploiting them. Techniques of interest include mechanisms for increased resilience to soft errors, statistically correct architectures for low power, and runtime systems that tradeoff solution quality for improved performance and/or real-time guarantees.



  • Donald Yeung
  • Students


  • Hameed Badawy
  • Xuanhua Li
  • Meng-Ju Wu
  • Xu Yang
  • Publications:

  • Stephen P. Crago and Donald Yeung. Reducing Data Movement with Approximate Computing Techniques. In Proceedings of the IEEE International Conference on Rebooting Computing. San Diego, CA. October 2016. (pdf)

  • Xuanhua Li and Donald Yeung. Exploiting Value Prediction for Fault Tolerance. In Proceedings of the 3rd Workshop on Dependable Architectures (WDA-III). Lake Como, Italy. November 2008. (pdf, gzip'd ps)

  • Xuanhua Li and Donald Yeung. Exploiting Application-Level Correctness for Low-Cost Fault Tolerance. Journal of Instruction-Level Parallelism. Vol. 10. pp. 1-28. September 2008. (pdf, gzip'd ps)

  • Xuanhua Li and Donald Yeung. Application-Level Correctness and its Impact on Fault Tolerance. In Proceedings of The 13th International Symposium on High-Performance Computer Architecture (HPCA-XIII). Phoenix, AZ. February 2007. (pdf, postscript)

  • Funding:

    This project is funded by the Defense Advanced Research Projects Agency (DARPA) through the Department of the Interior National Business Center under grant #NBCH104009.

    Last updated: October 2016 by Donald Yeung (