From the 1 of 10 linked papers with an AI index.
2 citations · 2 across the 5 of their papers we have counts for
8 papers
From Tool Invocation to Source-Mechanism Exploration: Protected White-Box DSE for Open-Source EDA
Zhiyu Zheng, Yiming Du, Ziyi Wang +1
The paper introduces ReviewDSE, a protected white-box design-space exploration framework that leverages source-level mechanisms in open-source EDA tools to improve placement qualit…
ORFS-agent: Tool-Using Agents for Chip Design Optimization
Amur Ghose, Andrew B. Kahng, Sayak Kundu +1
Machine learning has been widely used to optimize complex engineering workflows across numerous domains. In integrated circuit design, modern flows (e.g., register-transfer level t…
An Updated Assessment of Reinforcement Learning for Macro Placement
Chung-Kuan Cheng, Andrew B. Kahng, Sayak Kundu +2
We provide an improved assessment of Google Brain's deep reinforcement learning approach to macro placement and its updated Circuit Training (CT) implementation in GitHub. A strong…
ChipletPart: Cost-Aware Partitioning for 2.5D Systems
Alexander Graening, Puneet Gupta, Andrew B. Kahng +2
Industry adoption of chiplets has been growing as chiplets are a cost-effective option for making large, high-performance systems. Consequently, partitioning large systems into chi…
Invited: Toward Sustainable and Transparent Benchmarking for Academic Physical Design Research
Liwen Jiang, Andrew B. Kahng, Zhiang Wang +1
This paper presents RosettaStone 2.0, an open benchmark translation and evaluation framework built on OpenROAD-Research. RosettaStone 2.0 provides complete RTL-to-GDS reference flo…
Bridging the Initialization Gap: A Co-Optimization Framework for Mixed-Size Global Placement
Yuhao Ren, Yiting Liu, Yanfei Zhou +4
Global placement is a critical step with high computational complexity in VLSI physical design. Modern analytical placers formulate the placement problem as a nonlinear optimizatio…