This work from Google (original Nature paper: https://www.nature.com/articles/s41586-021-03544-w) has been credibly criticized by several researchers in the EDA CAD discipline. These papers are of interest:
- A rebuttal by a researcher within Google who wrote this at the same time as the "AlphaChip" work was going on ("Stronger Baselines for Evaluating Deep Reinforcement Learning in Chip Placement"): http://47.190.89.225/pub/education/MLcontra.pdf
- A paper from Igor Markov which critically evaluates the "AlphaChip" algorithm ("The False Dawn: Reevaluating Google's Reinforcement Learning for Chip Macro Placement"): https://arxiv.org/pdf/2306.09633
In short, the Google authors did not fairly evaluate their RL macro placement algorithm against other SOTA algorithms: rather they claim to perform better than a human at macro placement, which is far short of what mixed-placement algorithms are capable of today. The RL technique also requires significantly more compute than other algorithms and ultimately is learning a surrogate function for placement iteration rather than learning any novel representation of the placement problem itself.
- A rebuttal by a researcher within Google who wrote this at the same time as the "AlphaChip" work was going on ("Stronger Baselines for Evaluating Deep Reinforcement Learning in Chip Placement"): http://47.190.89.225/pub/education/MLcontra.pdf
- The 2023 ISPD paper from a group at UCSD ("Assessment of Reinforcement Learning for Macro Placement"): https://vlsicad.ucsd.edu/Publications/Conferences/396/c396.p...
- A paper from Igor Markov which critically evaluates the "AlphaChip" algorithm ("The False Dawn: Reevaluating Google's Reinforcement Learning for Chip Macro Placement"): https://arxiv.org/pdf/2306.09633
In short, the Google authors did not fairly evaluate their RL macro placement algorithm against other SOTA algorithms: rather they claim to perform better than a human at macro placement, which is far short of what mixed-placement algorithms are capable of today. The RL technique also requires significantly more compute than other algorithms and ultimately is learning a surrogate function for placement iteration rather than learning any novel representation of the placement problem itself.
In full disclosure, I am quite skeptical of their work and wrote a detailed post on my website: https://vighneshiyer.com/misc/ml-for-placement/