2 citations · 2 across the 3 of their papers we have counts for
3 papers
MAGE: Human-Like Macro Placement via Agentic Multimodal Reasoning
Andrew B. Kahng, Sayak Kundu, Bodhisatta Pramanik
Macro placement still requires substantial manual refinement in industrial physical design flows. We present MAGE (Macro Placement Agentic Engine), a multimodal multi-agent framewo…
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…