4 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…