1 citations · 4 across the 9 of their papers we have counts for
5 papers · 1 filter
How Can Reinforcement Learning Achieve Expert-level Placement?
Ruo-Tong Chen, Ke Xue, Chengrui Gao +7
Chip placement is a critical step in physical design. While reinforcement learning (RL)-based methods have recently emerged, their training primarily focuses on wirelength optimiza…
FlowPlace: Flow Matching for Chip Placement
Peng Xie, Ke Xue, Yunqi Shi +6
Chip placement plays an important role in physical design. While generative models like diffusion models offer promising learning-based solutions, current methods have the followin…
ReMaP: Macro Placement by Recursively Prototyping and Packing Tree-based Relocating
Yunqi Shi, Xi Lin, Zhiang Wang +8
This work introduces the ReMaP method, which generates expert-quality macro placements through recursively prototyping and packing tree-based relocating. We first perf…
Open3DBench: Open-Source Benchmark for 3D-IC Backend Implementation and PPA Evaluation
Yunqi Shi, Chengrui Gao, Wanqi Ren +6
This work introduces Open3DBench, an open-source 3D-IC backend implementation benchmark built upon the OpenROAD-flow-scripts framework, enabling comprehensive evaluation of power,…
Timing-Driven Global Placement by Efficient Critical Path Extraction
Yunqi Shi, Siyuan Xu, Shixiong Kai +4
Timing optimization during the global placement of integrated circuits has been a significant focus for decades, yet it remains a complex, unresolved issue. Recent analytical metho…