Floorplanning is a mandatory step in the design of hardware accelerators for FPGA platforms, especially when adopting dynamic partial reconfiguration (DPR). This paper presents FLORA, an automated floorplanner based on optimization via Mixed-Integer Linear Programming (MILP). The floorplanning problem is solved by means of a novel fine-grained modeling strategy of FPGA resources. Furthermore, differently from other proposals, our approach takes into account several realistic Partial Reconfiguration (PR) floorplanning constraints on FPGAs. FLORA was compared against state-of-the-art floorplanners by means of benchmark suites, showing that it is capable of providing better performance in terms of resource consumption, maximum inter-region, wire-length, and running time required to produce the solutions. Finally, FLORA was utilized to generate placements for a partially-reconfigurable video processing engine that was implemented on a Xilinx Zynq-7020.

Flora: Floorplan optimizer for reconfigurable areas in FPGAs

Seyoum B. B.;Biondi A.;Buttazzo G. C.
2019-01-01

Abstract

Floorplanning is a mandatory step in the design of hardware accelerators for FPGA platforms, especially when adopting dynamic partial reconfiguration (DPR). This paper presents FLORA, an automated floorplanner based on optimization via Mixed-Integer Linear Programming (MILP). The floorplanning problem is solved by means of a novel fine-grained modeling strategy of FPGA resources. Furthermore, differently from other proposals, our approach takes into account several realistic Partial Reconfiguration (PR) floorplanning constraints on FPGAs. FLORA was compared against state-of-the-art floorplanners by means of benchmark suites, showing that it is capable of providing better performance in terms of resource consumption, maximum inter-region, wire-length, and running time required to produce the solutions. Finally, FLORA was utilized to generate placements for a partially-reconfigurable video processing engine that was implemented on a Xilinx Zynq-7020.
2019
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11382/535066
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