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Taking Advantage of Correlation in Stochastic Computing

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Abstract

In recent years, shrinking size in integrated circuits has imposed a big challenge in maintaining the reliability in conventional computing. Stochastic computing has been seen as a reliable, low-cost, and low-power alternative to overcome such issues. Stochastic Computing (SC) computes data in the form of bit streams of 1s and 0s. Therefore, SC outperforms conventional computing in terms of tolerance to soft error and uncertainty at the cost of increased computational time. Stochastic Computing with uncorrelated input streams requires streams to be highly independent for better accuracy. This results in more hardware consumption for conversion of binary numbers to stochastic streams. Correlation can be used to design Stochastic Computation Elements (SCE) with correlated input streams. These designs have higher accuracy and less hardware consumption. In this paper, we propose new SC designs to implement image processing algorithms with correlated input streams. Experimental results of proposed SC with correlated input streams show on average 37% improvement in accuracy with reduction of 50-90% in area and 20-85% in delay over existing stochastic designs.
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Dates and versions

hal-01633725 , version 1 (14-11-2017)

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  • HAL Id : hal-01633725 , version 1

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Rahul Kumar Budhwani, Rengarajan Ragavan, Olivier Sentieys. Taking Advantage of Correlation in Stochastic Computing. ISCAS 2017 - IEEE International Symposium on Circuits and Systems, May 2017, Baltimore, United States. ⟨hal-01633725⟩
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