Abstract
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 chapter, we exam probabilistic-based design methodologies 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. Overall, the probabilistic-based design can dynamically adapt to structural and signal-based faults.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
S. Asai and Y. Wada. Technology challenges for inegration near and below 0.1 μm. Proceedings of the IEEE, 85(4):506, 1997.
P. Beckett and A. Jennings. Towards nanocomputer architecture. In Asia-Pacific Conference on Computer Systems Architecture, volume 6, pages 141–150, 2002.
Charles H. Bennett. The thermodynamics of computation—a review. Intl. Journal of Theoretical Physics, 21(12):905–940, 1982.
R. Chellappa. Markov random fields: theory and applications. Academic Press, 1993.
Andre DeHon. Array-based architecture for fet-based, nanoscale electronics. IEEE Transactions on Nanotechnology, 2(1):23–32, March 2003.
S. Folling, O. Turel, and K. Likharv. Single-electron latching switches as nanoscale synapses. In Proc. of Int. Joint Conf. on Neural Networks, pages 216–221, July 2001.
Stuart Geman and Kevin Kochanek. Dynamic programming and the graphical representation of error-correcting codes. IEEE Transactions on Information Theory, 47(2):549–568, February 2001.
S. C. Goldstein and M. Budiu. Nanofabrics: Spatial computing using molecular electronics. In The 28th Annual International Symposium on Computer Architecture, pages 178–189, 2001.
J. Hammersley and P. Clifford. Markov fields on finite graphs and lattices. Technical report, University of California, Berkeley, 1968.
Jie Han and Pieter Jonker. A system architecture solution for unreliable nanoelectronic devices. IEEE Transactions on Nanotechnology, 1(4):201–208, December 2002.
S. Z. Li. Markov Random Field Modeling in Computer Vision. Springer, 1995.
Samuel Luckenbull. Building bayesian netorks with analog subthreshold cmos circuits. http://zoo.cs.yale.edu/classes/cs490/01-02b/luckenbill.samuel.sbl23/.
R. R. Schulz and R. L. Stevenson. A Bayesian approach to image expansion for improved definition. IEEE Transactions on Image Processing, 3(3):233–242, 1994.
Y. Taur, D. Buchanan, and et. al. CMOS scaling into the nanometer regime. Proceedings of the IEEE, 85(4):486, 1997.
J. von Neumann. Probabilistic Logic and the Synthesis of Reliable Organisms from Unreliable Components. Automata Studies. Princeton University Press, 1956.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Kluwer Academic Publishers
About this chapter
Cite this chapter
Bahar, R.I., Chen, J., Mundy, J. (2004). A Probabilistic-Based Design for Nanoscale Computation. In: Shukla, S.K., Bahar, R.I. (eds) Nano, Quantum and Molecular Computing. Springer, Boston, MA. https://doi.org/10.1007/1-4020-8068-9_5
Download citation
DOI: https://doi.org/10.1007/1-4020-8068-9_5
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4020-8067-8
Online ISBN: 978-1-4020-8068-5
eBook Packages: Springer Book Archive