1 citations · 3 across the 5 of their papers we have counts for
5 papers
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…
BBOPlace-Bench: Benchmarking Black-Box Optimization for Chip Placement
Ke Xue, Ruo-Tong Chen, Rong-Xi Tan +5
Chip placement is a vital stage in modern chip design, and black-box optimization (BBO) has been applied to it for decades. Early BBO efforts, however, were limited by immature pro…
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…
Reinforcement Learning Policy as Macro Regulator Rather than Macro Placer
Ke Xue, Ruo-Tong Chen, Xi Lin +4
In modern chip design, placement aims at placing millions of circuit modules, which is an essential step that significantly influences power, performance, and area (PPA) metrics. R…
Escaping Local Optima in Global Placement
Ke Xue, Xi Lin, Yunqi Shi +3
Placement is crucial in the physical design, as it greatly affects power, performance, and area metrics. Recent advancements in analytical methods, such as DREAMPlace, have demonst…