TY - GEN
T1 - Using stochastic computing to implement digital image processing algorithms
AU - Li, Peng
AU - Lilja, David J.
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=83455220177&partnerID=8YFLogxK
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U2 - 10.1109/ICCD.2011.6081391
DO - 10.1109/ICCD.2011.6081391
M3 - Conference contribution
AN - SCOPUS:83455220177
SN - 9781457719523
T3 - Proceedings - IEEE International Conference on Computer Design: VLSI in Computers and Processors
SP - 154
EP - 161
BT - 2011 IEEE 29th International Conference on Computer Design, ICCD 2011
T2 - 29th IEEE International Conference on Computer Design 2011, ICCD 2011
Y2 - 9 November 2011 through 12 November 2011
ER -