MaPart: An Efficient Multi-FPGA System-Aware Hypergraph Partitioning Framework

Multi-FPGA systems (MFSs) are increasingly important in addressing VLSI circuit emulation and prototyping. However, the limitations of I/O resources between Field programmable gate arrays (FPGAs) have driven the usage of TDM and FPGA-hop technologies, which complicate the partitioning problem. Conse...

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Veröffentlicht in:IEEE transactions on computer-aided design of integrated circuits and systems 2024-10, Vol.43 (10), p.3212-3225
Hauptverfasser: Li, Benzheng, Bi, Shunyang, You, Hailong, Qi, Zhongdong, Guo, Guangxin, Sun, Richard, Zhang, Yuming
Format: Artikel
Sprache:eng
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Zusammenfassung:Multi-FPGA systems (MFSs) are increasingly important in addressing VLSI circuit emulation and prototyping. However, the limitations of I/O resources between Field programmable gate arrays (FPGAs) have driven the usage of TDM and FPGA-hop technologies, which complicate the partitioning problem. Consequently, designing a suitable partitioning process for MFS has emerged as a critical research question affecting overall system performance. This article proposes MaPart, a novel hypergraph partitioning framework, which aims to minimize the maximum path delay in MFS. MaPart combines binary search with a nonhop partitioner, TopoPart+, to minimize the maximum hop count during the partitioning process. Compared to previous nonhop partitioner, TopoPart+ provides enhanced problem-solving capabilities and achieves a remarkable 96% reduction in cut-size. Furthermore, the framework incorporates two successive local refinement algorithms that optimize the time-division multiplexing ratio, reduce total hop count, and alleviate congestion on critical paths. Additionally, MaPart includes a system-level router based on layered graphs, enabling flexible control of the hop count based on the timing criticality of each path. Experimental results demonstrate that the proposed framework achieves a significant 37% reduction in delay compared to baseline algorithms when evaluated using publicly available benchmarks.
ISSN:0278-0070
1937-4151
DOI:10.1109/TCAD.2024.3392758