DREAMPlaceFPGA: An Open-Source Analytical Placer for Large Scale Heterogeneous FPGAs using Deep-Learning Toolkit

Abstract

Modern Field Programmable Gate Arrays (FPGAs) are large-scale heterogeneous programmable devices that enable high performance and energy efficiency. Placement is a crucial and computationally intensive step in the FPGA design flow that determines the physical locations of various heterogeneous instances in the design. Several works have employed GPUs and FPGAs to accelerate FPGA placement and have obtained significant runtime improvement. However, with these approaches, it is a non-trivial effort to develop optimized and algorithmic-specific kernels for GPU and FPGA to realize the best acceleration performance. In this work, we present DREAMPlaceFPGA, an open-source deep-learning toolkit-based accelerated placement framework for large-scale heterogeneous FPGAs. Notably, we develop new operators in our framework to handle heterogeneous resources and FPGA architecture-specific legality constraints. The proposed framework requires low development cost and provides an extensible framework to employ different placement optimizations. Our experimental results on the ISPD'2016 benchmarks show very promising results compared to prior approaches.