152 citations · 154 across the 2 of their papers we have counts for
3 papers · 1 filter
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…
Transferable Graph Optimizers for ML Compilers
Yanqi Zhou, Sudip Roy, Amirali Abdolrashidi +9
Most compilers for machine learning (ML) frameworks need to solve many correlated optimization problems to generate efficient machine code. Current ML compilers rely on heuristics…
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…