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A reconfigurable stochastic architecture for highly reliable computing
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Great Lakes Symposium on VLSI archive
Proceedings of the 19th ACM Great Lakes symposium on VLSI table of contents
Boston Area, MA, USA
SESSION: System- and architectural-level optimization table of contents
Pages 315-320  
Year of Publication: 2009
ISBN:978-1-60558-522-2
Authors
Xin Li  University of Minnesota, Twin Cities, Minneapolis, MN, USA
Weikang Qian  University of Minnesota, Twin Cities, Minneapolis, MN, USA
Marc D. Riedel  University of Minnesota, Twin Cities, Minneapolis, MN, USA
Kia Bazargan  University of Minnesota, Twin Cities, Minneapolis, MN, USA
David J. Lilja  University of Minnesota, Twin Cities, Minneapolis, MN, USA
Sponsors
ACM: Association for Computing Machinery
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
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ABSTRACT

Mounting concerns over variability, defects and noise motivate a new approach for integrated circuits: the design of stochastic logic, that is to say, digital circuitry that operates on probabilistic signals, and so can cope with errors and uncertainty. Techniques for probabilistic analysis are well established. We advocate a strategy for synthesis. In this paper, we present a reconfigurable architecture that implements the computation of arbitrary continuous functions with stochastic logic. We analyze the sources of error: approximation, quantization, and random fluctuations. We demonstrate the effectiveness of our method on a collection of benchmarks for image processing. Synthesis trials show that our stochastic architecture requires less area than conventional hardware implementations. It achieves a large speed up compared to software conventional implementations. Most importantly, it is much more tolerant of soft errors (bit flips) than these deterministic implementations.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
W. Qian, J. Backes, and M. Riedel, "The synthesis of stochastic circuits for nanoscale computation," in IWLS'07, pp. 176--183.
 
2
J. von Neumann, "Probabilistic logics and the synthesis of reliable organisms from unreliable components," in Automata Studies. Princeton Univ. Press, 1956, pp. 43--98.
 
3
E. F. Moore and C. E. Williams, "Reliable circuits using less reliable relays," J. Franklin Inst., vol. 262, pp. 191--208, 281--297, 1956.
4
 
5
 
6
C. C. Lee, "Fuzzy logic in control systems: Fuzzy logic controller - part I," IEEE Transactions on Systems, Man and Cybernetics, vol. 20, no. 2, pp. 404--418, 1990.
 
7
B. Gaines, "Stochastic computing systems," in Advances in Information Systems Science. Plenum, 1969, vol. 2, ch. 2, pp. 37--172.
 
8
9
 
11
G. Lorentz, Bernstein Polynomials. University of Toronto Press, 1953.
 
12
J. Ortega, C. Janer, J. Quero, L. Franquelo, J. Pinilla, and J. Serrano, "Analog to digital and digital to analog conversion based on stochastic logic," in IECON'95, pp. 995--999.
 
13
 
14
Irotek, "EasyRGB," 2008. {Online}. Available: http://www.easyrgb.com/index.php?X=MATH
 
15
D. Phillips, Image Processing in C. R & D Publications, 1994.
 
16
T. Urabe, "3D Examples," 2002. {Online}. Available: http://mathmuse.sci.ibaraki.ac.jp/geom/param1E.html

Collaborative Colleagues:
Xin Li: colleagues
Weikang Qian: colleagues
Marc D. Riedel: colleagues
Kia Bazargan: colleagues
David J. Lilja: colleagues