Proceedings of the International Conference on Computer-Aided Design
The synthesis of complex arithmetic computation on stochastic bit streams using sequential logic
Proceedings of the International Conference on Computer-Aided Design
Comparing the performance of stochastic simulation on GPUs and OpenMP
International Journal of Computational Science and Engineering
Stochastic circuits for real-time image-processing applications
Proceedings of the 50th Annual Design Automation Conference
Binary stochastic implementation of digital logic
Proceedings of the 2014 ACM/SIGDA international symposium on Field-programmable gate arrays
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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.