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Performance characteristics of the Monte-Carlo clustering processor (MCCP) - a field programmable logic based custom computing machine

  • C. P. Cowen
  • S. Monaghan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 849)

Abstract

A special purpose processor originally designed for Monte-Carlo simulation using Metropolis type algorithms has been reconfigured to allow the use of a new improved class of Monte-Carlo algorithm without compromising the processor's performance.

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References

  1. 1.
    S. Monaghan, T. O'Brien, and P. Noakes. FPGAs, page 363. Abingdon CS Books, 1991. Edited by W. Moore and W. Luk.Google Scholar
  2. 2.
    S. Monaghan. Gate level reconfigurable Monte Carlo processor. JVSP, 6:139–153, 1993.Google Scholar
  3. 3.
    Xilinx. The Programmable Gate Array Data Book, 1991.Google Scholar
  4. 4.
    K. Binder, editor. Applications of the Monte-Carlo Methods in Statistical Physics. Springer-Verlag, 1984.Google Scholar
  5. 5.
    S. Monaghan and C.P. Cowen. Multi-bit reconfigurable processor for DSP applications in Statistical Physics. Proc. FPGAs for Custom Computing Machines FCCM'93, 1993. sponsored by IEEE Computer Society.Google Scholar
  6. 6.
    Robert H. Swendsen and Jian-Sheng Wang. Nonuniversal Critical Dynamics in Monte Carlo Simulations. Physical Review Letters, 58(2), January 1987.Google Scholar
  7. 7.
    C.P. Cowen and S. Monaghan. A Reconfigurable Monte-Carlo Clustering Processor (MCCP). Proc. FPGAs for Custom Computing Machines FCCM'94, 1994. sponsored by IEEE Computer Society.Google Scholar
  8. 8.
    Analog Devices. ADSP-2101 Data Sheet, 1990.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • C. P. Cowen
    • 1
  • S. Monaghan
    • 1
  1. 1.Neural and VLSI Systems Group Department of Electronic Systems EngineeringUniversity of EssexColchesterEngland

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