A Probabilistic-Based Design Methodology for Nanoscale Computation

  • Authors:
  • R. Iris Bahar;Joseph Mundy;Jie Chen

  • Affiliations:
  • Brown University, RI;Brown University, RI;Brown University, RI

  • Venue:
  • Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
  • Year:
  • 2003

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Abstract

As current silicon-based techniques fast approach their practicallimits, the investigation of nanoscale electronics, devices andsystem architectures becomes a central research priority. It is expectedthat nanoarchitectures will confront devices and interconnectionswith high inherent defect rates, which motivates the searchfor new architectural paradigms.In this paper, we propose a probabilistic-based design methodologyfor designing nanoscale computer architectures based onMarkov Random Fields (MRF). The MRF can express arbitrarylogic circuits and logic operation is achieved by maximizing theprobability of state configurations in the logic network. Maximizingstate probability is equivalent to minimizing a form of energythat depends on neighboring nodes in the network. Once we developa library of elementary logic components, we can link themtogether to build desired architectures based on the belief propagationalgorithm. Belief propagation is a way of organizing theglobal computation of marginal belief in terms of smaller localcomputations. We will illustrate the proposed design methodologywith some elementary logic examples.