Re:Jeff Dean Responds to EDA Industry about AlphaChip
by dogleg77 on Sunday December 01, 2024 @07:41PM
(#42291966)
Reading those papers and looking at the code, it doesn't look easy. However, let's imagine that the Cheng et al team comes back with results for pretraining a few months from now, and they support the conclusions of their earlier paper. What should they do to help everyone reach a conclusion?
Re:Jeff Dean Responds to EDA Industry about AlphaChip
by wholehog on Monday December 02, 2024 @01:25AM
(#42293649)
Pre-training is just training on multiple chips.
"If Cheng et al. had reached out to the corresponding authors of the Nature paper, we would have gladly helped them to correct these issues prior to publication" (https://arxiv.org/pdf/2411.10053)
That's how you actually do a reproduction study - you reach out to the corresponding authors and make sure you do everything exactly the same. But at this point, it's hard to imagine the AlphaChip folks having much patience with them.
Likewise the importance of spending 20x as much money on the training portion seems easy to verify, and significant.
That they would fail to properly test against industry standard workbenches seems reasonable to me. This is a bunch of ML specialists who know nothing about chip design. Their background is beating everyone at Go and setting a new state of the art for protein folding, and not chip design. If you dismiss those particular past accomplishments as hyperbolic marketing, that's your decision. But you aren't going to find a lot of people in these parts who agree with you.
If you think that those were real, but that a bunch of more recent accomplishments are BS, I haven't been following closely enough to have an opinion. The stuff that crossed my radar since AlphaFold is mostly done at places like OpenAI, and not Google.
Regardless, the truth will out. And what Google is claiming for itself here really isn't all that impressive.