elfPlace is a flat nonlinear placement algorithm for large-scale heterogeneous field-programmable gate arrays (FPGAs). We adopt the analogy between placement and electrostatic systems initially proposed by ePlace and extend it… Click to show full abstract
elfPlace is a flat nonlinear placement algorithm for large-scale heterogeneous field-programmable gate arrays (FPGAs). We adopt the analogy between placement and electrostatic systems initially proposed by ePlace and extend it to tackle heterogeneous blocks in FPGA designs. To achieve satisfiable solution quality with fast and robust numerical convergence, an augmented Lagrangian formulation together with a preconditioning technique and a normalized subgradient-based multiplier updating scheme are proposed. Besides pure-wirelength minimization, we also propose a unified instance area adjustment scheme to simultaneously optimize routability, pin density, and downstream clustering compatibility. We further propose run-to-run deterministic GPU acceleration techniques to speedup the global placement. Our experiments on the ISPD 2016 benchmark suite show that elfPlace outperforms four state-of-the-art FPGA placers UTPlaceF, RippleFPGA, GPlace3.0, and UTPlaceF-DL by 13.5%, 10.2%, 8.8%, and 7.0%, respectively, in routed wirelength with competitive runtime.
