A probabilistic-based design methodology for nanoscale computation

As current silicon-based techniques fast approach their practical limits, the investigation of nanoscale electronics, devices and system architectures becomes a central research priority. It is expected that nanoarchitectures will confront devices and interconnections with high inherent defect rates, which motivates the search for new architectural paradigms. In this paper, we propose a probabilistic-based design methodology for designing nanoscale computer architectures based on Markov Random Fields (MRF). The MRF can express arbitrary logic circuits and logic operation is achieved by maximizing the probability of state configurations in the logic network. Maximizing state probability is equivalent to minimizing a form of energy that depends on neighboring nodes in the network. Once we develop a library of elementary logic components, we can link them together to build desired architectures based on the belief propagation algorithm. Belief propagation is a way of organizing the global computation of marginal belief in terms of smaller local computations. We will illustrate the proposed design methodology with some elementary logic examples.