On the Theory of Stochastic Processors

Traditional architecture design approaches hide hardware uncertainties from the software stack through over-design, which is often expensive in terms of power consumption. The recently proposed quantitative alternative of stochastic computing requires circuits and processors to be correct only probabilistically and use less power. In this paper, we present the first step towards a theory of stochastic computing. Specifically, a formal model of a device which computes a deterministic function with stochastic delays is presented; the semantics of a stochastic circuit is obtained by composing such devices; finally, a quantitative notion of stochastic correctness, called correctness factor (CF), is introduced. For random data sources, a closed form expression is derived for CF of devices, which shows that there are two probabilities that contribute positively, namely, the probability of being timely with current inputs and the probability of being lucky with past inputs. We show the characteristic graphs obtained from the analytical expressions for the variation of correctness factor with clock period, for several simple circuits and sources.

[1]  Marc Tremblay,et al.  The implementation and application of micro rollback in fault-tolerant VLSI systems , 1988, [1988] The Eighteenth International Symposium on Fault-Tolerant Computing. Digest of Papers.

[2]  Anna Philippou,et al.  Tools and Algorithms for the Construction and Analysis of Systems , 2018, Lecture Notes in Computer Science.

[3]  Joost-Pieter Katoen,et al.  Bisimulation Minimisation Mostly Speeds Up Probabilistic Model Checking , 2007, TACAS.

[4]  James Tschanz,et al.  Parameter variations and impact on circuits and microarchitecture , 2003, Proceedings 2003. Design Automation Conference (IEEE Cat. No.03CH37451).

[5]  Nancy A. Lynch,et al.  Proving Approximate Implementations for Probabilistic I/O Automata , 2007, PDPAR/PaUL@FLoC.

[6]  Joseph L. Mundy,et al.  Designing logic circuits for probabilistic computation in the presence of noise , 2005, Proceedings. 42nd Design Automation Conference, 2005..

[7]  Dhiraj K. Pradhan,et al.  Fault-Tolerant Design Strategies for High Reliability and Safety , 1993, IEEE Trans. Computers.

[8]  Tomás Feder,et al.  Reliable computation by networks in the presence of noise , 1989, IEEE Trans. Inf. Theory.

[9]  John Sartori,et al.  Slack redistribution for graceful degradation under voltage overscaling , 2010, 2010 15th Asia and South Pacific Design Automation Conference (ASP-DAC).

[10]  Nicholas Pippenger,et al.  Reliable computation by formulas in the presence of noise , 1988, IEEE Trans. Inf. Theory.

[11]  Douglas L. Jones,et al.  Stochastic computation , 2010, Design Automation Conference.

[12]  Michael Huth,et al.  Tools and Algorithms for the Construction and Analysis of Systems, 13th International Conference, TACAS 2007, Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2007 Braga, Portugal, March 24 - April 1, 2007, Proceedings , 2007, TACAS.

[13]  Joël Ouaknine,et al.  An Intrinsic Characterization of Approximate Probabilistic Bisimilarity , 2003, FoSSaCS.

[14]  Stanislaw J. Piestrak,et al.  Design of Fast Self-Testing Checkers for a Class of Berger Codes , 1987, IEEE Transactions on Computers.

[15]  John Sartori,et al.  Designing a processor from the ground up to allow voltage/reliability tradeoffs , 2010, HPCA - 16 2010 The Sixteenth International Symposium on High-Performance Computer Architecture.

[16]  Weikang Qian,et al.  The synthesis of robust polynomial arithmetic with stochastic logic , 2008, 2008 45th ACM/IEEE Design Automation Conference.

[17]  D. Blaauw,et al.  Opportunities and challenges for better than worst-case design , 2005, Proceedings of the ASP-DAC 2005. Asia and South Pacific Design Automation Conference, 2005..

[18]  John Sartori,et al.  Recovery-driven design: A power minimization methodology for error-tolerant processor modules , 2010, Design Automation Conference.

[19]  Joost-Pieter Katoen,et al.  The Ins and Outs of the Probabilistic Model Checker MRMC , 2009, 2009 Sixth International Conference on the Quantitative Evaluation of Systems.

[20]  Leonard J. Schulman,et al.  Signal propagation, with application to a lower bound on the depth of noisy formulas , 1993, Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science.

[21]  J. von Neumann,et al.  Probabilistic Logic and the Synthesis of Reliable Organisms from Unreliable Components , 1956 .

[22]  Andrew Hinton,et al.  PRISM: A Tool for Automatic Verification of Probabilistic Systems , 2006, TACAS.