High-Performance Placement Engine for Modern Large-Scale FPGAs With Heterogeneity and Clock Constraints

Ziran Zhu, Yangjie Mei, Kangkang Deng, Huan He, Jianli Chen, Jun Yang, Yao-Wen Chang

Abstract: As field-programmable gate array (FPGA) architectures continue to evolve and become more complex, the heterogeneity and clock constraints imposed by modern FPGAs have posed significant challenges to FPGA placement. This article proposes a high-performance placement engine for modern large-scale FPGAs with heterogeneity and clock constraints. To improve efficiency and scalability, we develop a clustering method considering both internal/external connectivity and the balance of block types to build the hierarchy. In each hierarchy level, we propose a hybrid penalty and augmented Lagrangian method (HPALM) to convert the FPGA global placement with heterogeneity and clock constraints into a series of unconstrained optimization subproblems, then use the Adam method to solve each subproblem. In particular, we prove that the HPALM is globally convergent for global placement. Besides, a matching-based IP block legalization is developed to legalize the DSPs and RAMs, and a multistage packing is presented to cluster LUTs and FFs into HCLBs. Finally, we propose a history-based legalization to legalize CLBs in an FPGA, and a simulated-annealing-based detailed placement is presented to reduce the wirelength while maintaining legality. Compared with the state-of-the-art works, experimental results based on the ISPD 2017 contest benchmarks show that the proposed algorithm can achieve the shortest routed wirelength in a reasonable runtime.