152 citations · 154 across the 3 of their papers we have counts for
3 papers · 1 filter
See it to Place it: Evolving Macro Placements with Vision-Language Models
Ikechukwu Uchendu, Swati Goel, Karly Hou +5
We propose using Vision-Language Models (VLMs) for macro placement in chip floorplanning, a complex optimization task that has recently shown promising advancements through machine…
Delving into Macro Placement with Reinforcement Learning
Zixuan Jiang, Ebrahim Songhori, Shen Wang +5
In physical design, human designers typically place macros via trial and error, which is a Markov decision process. Reinforcement learning (RL) methods have demonstrated superhuman…
Chip Placement with Deep Reinforcement Learning
Azalia Mirhoseini, Anna Goldie, Mustafa Yazgan +19
In this work, we present a learning-based approach to chip placement, one of the most complex and time-consuming stages of the chip design process. Unlike prior methods, our approa…