Using stochastic computing to implement digital image processing algorithms

Peng Li, David J Lilja

Research output: Chapter in Book/Report/Conference proceedingConference contribution

107 Scopus citations


As device scaling continues to nanoscale dimensions, circuit reliability will continue to become an ever greater problem. Stochastic computing, which performs computing with random bits (stochastic bits streams), can be used to enable reliable computation using those unreliable devices. However, one of the major issues of stochastic computing is that applications implemented with this technique are limited by the available computational elements. In this paper, first we will introduce and prove a stochastic absolute value function. Second, we will demonstrate a mathematical analysis of a stochastic tanh function, which is a key component used in a stochastic comparator. Third, we will present a quantitative analysis of a one-parameter linear gain function, and propose a new two-parameter version. The validity of the present stochastic computational elements is demonstrated through four basic digital image processing algorithms: edge detection, frame difference based image segmentation, median filter based noise reduction, and image contrast stretching. Our experimental results show that stochastic implementations tolerate more noise and consume less hardware than their conventional counterparts.

Original languageEnglish (US)
Title of host publication2011 IEEE 29th International Conference on Computer Design, ICCD 2011
Number of pages8
StatePublished - Dec 19 2011
Event29th IEEE International Conference on Computer Design 2011, ICCD 2011 - Amherst, MA, United States
Duration: Nov 9 2011Nov 12 2011

Publication series

NameProceedings - IEEE International Conference on Computer Design: VLSI in Computers and Processors
ISSN (Print)1063-6404


Other29th IEEE International Conference on Computer Design 2011, ICCD 2011
Country/TerritoryUnited States
CityAmherst, MA


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