Wang, HuiminHuiminWangTong, XingyuXingyuTongMa, ChenyueChenyueMaShi, RunmingRunmingShiChen, JianliJianliChenWang, KunKunWangYu, JunJunYuYAO-WEN CHANG2023-06-152023-06-152022-07-1097814503914290738100Xhttps://scholars.lib.ntu.edu.tw/handle/123456789/632699The fast-growing capacity and complexity are challenging for FPGA global placement. Besides, while many recent studies have focused on the eDensity-based placement as its great efficiency and quality, they suffer from redundant frequency translation. This paper presents a CNN-inspired analytical placement algorithm to effectively handle the redundant frequency translation problem for large-scale FPGAs. Specifically, we compute the density penalty by a fully-connected propagation and gradient to a discrete differential convolution backward. With the FPGA heterogeneity, vectorization plays a vital role in self-adjusting the density penalty factor and the learning rate. In addition, a pseudo net model is used to further optimize the site constraints by establishing connections between blocks and their nearest available regions. Finally, we formulate a refined objective function and a degree-specific gradient preconditioning to achieve a robust, high-quality solution. Experimental results show that our algorithm achieves an 8% reduction on HPWL and 15% less global placement runtime on average over leading commercial tools.conference paper10.1145/3489517.35305662-s2.0-85137485293https://api.elsevier.com/content/abstract/scopus_id/85137485293