Computation on Stochastic Bit Streams Digital Image Processing Case Studies

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.

[1]  Bruce F. Cockburn,et al.  A scalable LDPC decoder ASIC architecture with bit-serial message exchange , 2008, Integr..

[2]  Larry S. Davis,et al.  Non-parametric Model for Background Subtraction , 2000, ECCV.

[3]  David J. Lilja,et al.  A low power fault-tolerance architecture for the kernel density estimation based image segmentation algorithm , 2011, ASAP 2011 - 22nd IEEE International Conference on Application-specific Systems, Architectures and Processors.

[4]  D BrownBradley,et al.  Stochastic Neural Computation II , 2001 .

[5]  Howard C. Card,et al.  Stochastic Neural Computation II: Soft Competitive Learning , 2001, IEEE Trans. Computers.

[6]  Kia Bazargan,et al.  The synthesis of complex arithmetic computation on stochastic bit streams using sequential logic , 2012, 2012 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).

[7]  Vincent C. Gaudet,et al.  Stochastic iterative decoders , 2005, Proceedings. International Symposium on Information Theory, 2005. ISIT 2005..

[8]  Howard C. Card,et al.  Stochastic Neural Computation I: Computational Elements , 2001, IEEE Trans. Computers.

[9]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[10]  David J. Lilja,et al.  Measuring computer performance : A practitioner's guide , 2000 .

[11]  Hao Chen,et al.  Stochastic computational models for accurate reliability evaluation of logic circuits , 2010, GLSVLSI '10.

[12]  Xin Li,et al.  An Architecture for Fault-Tolerant Computation with Stochastic Logic , 2011, IEEE Transactions on Computers.

[13]  Shie Mannor,et al.  Stochastic decoding of LDPC codes , 2006, IEEE Communications Letters.

[14]  Shie Mannor,et al.  Fully Parallel Stochastic LDPC Decoders , 2008, IEEE Transactions on Signal Processing.

[15]  Rafael C. González,et al.  Digital image processing, 3rd Edition , 2008 .

[16]  Peng Li,et al.  A stochastic reconfigurable architecture for fault-tolerant computation with sequential logic , 2012, 2012 IEEE 30th International Conference on Computer Design (ICCD).

[17]  Kia Bazargan,et al.  The synthesis of linear Finite State Machine-based Stochastic Computational Elements , 2012, 17th Asia and South Pacific Design Automation Conference.

[18]  Brian R. Gaines,et al.  Stochastic Computing Systems , 1969 .

[19]  W.J. Gross,et al.  Stochastic Implementation of LDPC Decoders , 2005, Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005..

[20]  Kia Bazargan,et al.  Using Two-Dimensional Finite State Machine for Stochastic Computation , 2012 .

[21]  David J. Lilja,et al.  Using stochastic computing to implement digital image processing algorithms , 2011, 2011 IEEE 29th International Conference on Computer Design (ICCD).

[22]  Vincent C. Gaudet,et al.  Iterative decoding using stochastic computation , 2003 .

[23]  Hao Chen,et al.  A Transistor-Level Stochastic Approach for Evaluating the Reliability of Digital Nanometric CMOS Circuits , 2011, 2011 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems.

[24]  G. Verghese,et al.  Mass fluctuation kinetics: capturing stochastic effects in systems of chemical reactions through coupled mean-variance computations. , 2007, The Journal of chemical physics.

[25]  Hui Li,et al.  A Stochastic-Based FPGA Controller for an Induction Motor Drive With Integrated Neural Network Algorithms , 2008, IEEE Transactions on Industrial Electronics.