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Stochastic Computing (SC) is an unconventional computing paradigm processingdata in the form of random bit-streams. The accuracy and energy efficiency ofSC systems highly depend on the stochastic number generator (SNG) unit thatconverts the data from conventional binary to stochastic bit-streams. Recentwork has shown significant improvement in the efficiency of SC systems byemploying low-discrepancy (LD) sequences such as Sobol and Halton sequences inthe SNG unit. Still, the usage of many well-known random sequences for SCremains unexplored. This work studies some new random sequences for potentialapplication in SC. Our design space exploration proposes a promising randomnumber generator for accurate and energy-efficient SC. We propose P2LSG, alow-cost and energy-efficient Low-discrepancy Sequence Generator derived fromPowers-of-2 VDC (Van der Corput) sequences. We evaluate the performance of ournovel bit-stream generator for two SC image and video processing case studies:image scaling and scene merging. For the scene merging task, we propose a novelSC design for the first time. Our experimental results show higher accuracy andlower hardware cost and energy consumption compared to the state-of-the-art.

P2LSG: Powers-of-2 Low-Discrepancy Sequence Generator for Stochastic Computing