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.
- Earlier Workshop Paper:
Xuanhua Li and Donald Yeung. Exploiting Soft Computing for
Increased Fault Tolerance. Appears in Proceedings of the
2006 Workshop on Architectural Support for Gigascale
Integration(ASGI'06). co-held with the ISCA-33. Boston, MA.
June 2006. (pdf, postscript)
This project is funded by the Defense Advanced Research Projects
Agency (DARPA) through the Department of the Interior National
Business Center under grant #NBCH104009.