[negativeonehalf]

For a more thorough discussion on pre-training, see this ISPD 2022 paper by the AlphaChip people: https://dl.acm.org/doi/pdf/10.1145/3505170.3511478

As for external usage of the method - MediaTek is one of the largest chip design companies in the world, and they built on AlphaChip. There's a quote from a MediaTek SVP at the bottom of the GDM blog post:

"AlphaChip's groundbreaking AI approach revolutionizes a key phase of chip design. At MediaTek, we've been pioneering chip design's floorplanning and macro placement by extending this technique in combination with the industry's best practices. This paradigm shift not only enhances design efficiency, but also sets new benchmarks for effectiveness, propelling the industry towards future breakthroughs."

[dogleg77]

Science is not done by quotes from VPs, and we don't know how MediaTek used these methods. Also, would you like to hear from VPs who wasted their company resources on Google RL and gave up?

The more marketing claims we see, the less compelling the Google story is.

Your perseverance is as admirable as it is suspicious. You are the lonely voice here defending the Google announcement.

[negativeonehalf]

Unfortunately, there aren't publicly available benchmarks for modern technology node sizes, at least not that I'm aware of. Kahng compared on 45nm and 12nm chips, which are very different from a physical design perspective from the 4nm technology node size used by Dimensity 5G, or the sub-10nm technology node size of TPU. "MLContra" used a >100nm technology node size, which is just crazy.

Even if the AlphaChip authors redid Kahng's study properly, this still wouldn't give us useful information -- what matters is AlphaChip's ability to optimize chips in a real-life, production setting, for modern chips, where millions of dollars are on the line.