Accelerating Monte Carlo based SSTA using FPGA

Authors:
Jason Cong;Karthik Gururaj;Wei Jiang;Bin Liu;Kirill Minkovich;Bo Yuan;Yi Zou
Affiliations:
University of California, Los Angeles, Los Angeles, CA, USA;University of California, Los Angeles, Los Angeles, CA, USA;University of California, Los Angeles, Los Angeles, CA, USA;University of California, Los Angeles, Los Angeles, CA, USA;University of California, Los Angeles, Los Angeles, CA, USA;University of California, Los Angeles, Los Angeles, CA, USA;University of California, Los Angeles, Los Angeles, CA, USA
Venue:
Proceedings of the 18th annual ACM/SIGDA international symposium on Field programmable gate arrays
Year:
2010

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Abstract

Monte Carlo based SSTA serves as the golden standard against alternative SSTA algorithms, but it is seldom used in practice due to its high computation time. In this paper, we accelerate Monte Carlo based SSTA using the FPGA platform. A simple dataflow pipeline technique will not work well due to the excessive usage of FPGA logic slices. We leverage the recently proposed pattern matching method to identify common circuit structures, and further use a mathematical programming based formulation to explore the trade-off between performance and logic slices consumption. The proposed design provides two orders of magnitude speedup compared to the CPU-based implementation.