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Hacker News Hacker NewsIn short, I think the Nature authors have made some reasonable criticisms regarding the training methodology employed by the ISPD authors, but the extreme compute cost and runtime of AlphaChip still makes it non-competitive with commercial autofloorplanners and AutoDMP. Regardless, I think the ISPD authors owe the Nature authors an even more rigorous study that addresses all their criticisms. Even if they just try to evaluate the pre-trained checkpoint that Google published, that would be a useful piece of data to add to the debate.
This is easy to debunk from the Google side: release a tool. If you don't want to release a tool, then it's unsubstantiated and you don't get to publish. Simple.
That having been said:
1) None of these "AI" tools have yet demonstrated the ability to classify "This is datapath", "This is array logic", "This is random logic". This is the BIG win. And it won't just be a couple of percentage points in area or a couple of days saved when it works--it will be 25%+ in area and months in time.
2) Saving a couple of percentage points in random logic isn't impressive. If I have the compute power to run EDA tools with a couple of different random seeds, at least one run will likely be a couple percentage points better.
3) I really don't understand why they don't do stuff on analog/RF. The patterns are smaller and much better matches to the kind of reinforcement learning that current "AI" is suited for.
I put this snake oil in the same category as "financial advice"--if it worked, they wouldn't be sharing it and would simply be printing money by taking advantage of it.
Like, for a CPU, you want to be sure it behaves properly for the given inputs. Anyone remember that floating point error in, was it Pentium IIs or Pentium IIIs?
I mean, I guess if the chip is designed for AI, and AIs are inherently nonguaranteed output/responses, then the AI chip design being nonguaranteed isn't any difference in nonguarantees.
Unless it is...