MAQO: A Scalable Many-Core Annealer for Quadratic Optimization
2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)(2022)
摘要
The end of Dennard Scaling has led to the increased development of domain-specific hardware for difficult tasks such as combinatorial optimization. This paper proposes a scalable CMOS architecture for solving NP-hard permutation optimization problems through careful design of a custom processing core in combination with a Parallel Tempering scheduler. A 32-core variant of this architecture is implemented on a Stratix 10 FPGA, operating at 220MHz with a peak power draw of 40W, and is shown to perform up to 4 times faster and with 40 times higher efficiency than the same algorithm implemented on a 64-core CPU with SIMD.
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关键词
Annealing Processor,Parallel Tempering,Stochastic Local Search,Quadratic Assignment Problem
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