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Related Experiment Videos

Markov chain algorithms: a template for building future robust low-power systems.

Biplab Deka1, Alex A Birklykke2, Henry Duwe3

  • 1Department of Electrical and Computer Engineering, University of Illinois at Urbana Champaign, Champaign, IL, USA deka2@illinois.edu.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|May 21, 2014
PubMed
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Future low-power computing may use unreliable devices. This study proposes using Markov chains (MCs) as algorithms that tolerate errors, demonstrating robustness in applications like sorting and decoding for reliable post-CMOS systems.

Area of Science:

  • Computer Engineering
  • Algorithm Design
  • Reliability Engineering

Background:

  • Post-CMOS devices promise lower power consumption but introduce inherent unreliability.
  • Designing robust systems with unreliable components requires significant power overheads for error correction.
  • Algorithmic approaches that tolerate computational errors are crucial for future low-power systems.

Purpose of the Study:

  • To propose a method for designing robust computational systems using unreliable post-CMOS devices.
  • To demonstrate the suitability of Markov chains (MCs) as an algorithmic template for error-tolerant computing.
  • To evaluate the robustness of MC-based algorithms against transition errors.

Main Methods:

  • Casting diverse applications as stochastic algorithms based on Markov chains (MCs).
Keywords:
Markov chainalgorithmic fault toleranceerror tolerance

Related Experiment Videos

  • Examples include Boolean satisfiability, sorting, low-density parity-check decoding, and clustering.
  • Employing algorithmic fault injection techniques to simulate transition errors.

    • Main Results:

    • Successfully demonstrated that applications can be effectively represented as MC algorithms.
    • Validated the robustness of these MC implementations against high rates of transition errors.
    • Showcased the inherent error tolerance of stochastic algorithms based on Markov chains.

    Conclusions:

    • Markov chains (MCs) offer a viable algorithmic template for building robust, low-power computational systems.
    • The proposed approach mitigates the challenges posed by unreliable post-CMOS devices.
    • Stochastic algorithms based on MCs are a promising direction for future reliable computing architectures.