Computation on stochastic bit streams digital image processing case studies

Peng Li, David J Lilja, Weikang Qian, Kia Bazargan, Marc Riedel

Research output: Contribution to journalArticle

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Abstract

Maintaining the reliability of integrated circuits as transistor sizes continue to shrink to nanoscale dimensions is a significant looming challenge for the industry. Computation on stochastic bit streams, which could replace conventional deterministic computation based on a binary radix, allows similar computation to be performed more reliably and often with less hardware area. Prior work discussed a variety of specific stochastic computational elements (SCEs) for applications such as artificial neural networks and control systems. Recently, very promising new SCEs have been developed based on finite-state machines (FSMs). In this paper, we introduce new SCEs based on FSMs for the task of digital image processing. We present five digital image processing algorithms as case studies of practical applications of the technique. We compare the error tolerance, hardware area, and latency of stochastic implementations to those of conventional deterministic implementations using binary radix encoding. We also provide a rigorous analysis of a particular function, namely the stochastic linear gain function, which had only been validated experimentally in prior work.

Original languageEnglish (US)
Article number6502263
Pages (from-to)449-462
Number of pages14
JournalIEEE Transactions on Very Large Scale Integration (VLSI) Systems
Volume22
Issue number3
DOIs
StatePublished - Mar 2014

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Keywords

  • Digital image processing
  • fault tolerance
  • finite state machine (FSM)
  • stochastic computing

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