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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...
Earlier quoted context omitted.
> they couldn't replicate it because they're idiots If they did not follow the steps to replicate (pre-training, using less compute, etc.) and then failed, so what's wrong with calling out the flaws in their attempted "replication"?
It's not a value judgement, just doesn't help his case at all. He'd need to counter the replication problem, but apparently that's not an option. Instead, he's making people who were unable to replicate it look bad, which actually strengthens their criticism.
How the hell would you verify an AI-generated silicon design? 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 i…
The same way you verify a human-generated one.
> Anyone remember that floating point error in, was it Pentium IIs or Pentium IIIs?
That was 1994. The industry has come a long way in the intervening 30 years.
How the hell would you verify an AI-generated silicon design? 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 i…
How the hell would you verify an AI-generated silicon design? 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 i…
I think you're asking a different question, but in the context of the OP researchers are exploring AI for solving deterministic but intractable problems in the field of chip design and not generating designs end to end.
Here's an excerpt from the paper.
"The objective is to place a netlist graph of macros (e.g., SRAMs) and standard cells (logic gates, such as NAND, NOR, and XOR) onto a chip canvas, such that power, performance, and area (PPA) are optimized, while adhering to constraints on placement density and routing congestion (described in Sections 3.3.6 and 3.3.5). Despite decades of research on this problem, it is still necessary for human experts to iterate for weeks with the existing placement tools, in order to produce solutions that meet multi-faceted design criteria."
The hope is that Reinforcement Learning can find solutions to such complex optimization problems.
I get why Jeff would be pressed to comment on this, given he's credited on basically all of "Google Brain" research output. But saying "they couldn't replicate it because they're idiots, therefore it's replicable" is not a rebuttal, just bullying. Sounds like the critics struck a nerve and there's no good way for him to refute the replication problem his research apparently exhibits.
Earlier quoted context omitted.
At the end of the day my question is simply why does anyone care about the drama over this one way or another? Either the research is as much of a breakthrough as is claimed and Google is about to pull way ahead of all these other "idiots" who can't replicate their method even when it is described to them in detail, or the research is flawed and overblown and not as effective as claimed. This seems like exactly the s…
The reason Jeff Dean cares is that his team's improvement compared to standard EDA tools was marginal at best and may have overfitted to a certain class of chips. Thus, he is defending his research because it is not widely accepted. Open source code has been out for years and in that time the EDA companies have largely done their own ML-based approaches that do not match his. He attributes this not to failings in his…
https://sparktoro.com/blog/is-google-losing-search-market-sh...
Earlier quoted context omitted.
They have literally been caught faking AI demos, they brought distrust on themselves.
Really not sure how you’re conflating product demos which are known to be pie in the sky across the industry (not just Google) with peer reviewed research published in journals. Super basic distinction imho.
Peer review doesn't mean as much as Elsevier would like you to believe. Plenty of peer-reviewed research is absolute trash.
Earlier quoted context omitted.
At the end of the day my question is simply why does anyone care about the drama over this one way or another? Either the research is as much of a breakthrough as is claimed and Google is about to pull way ahead of all these other "idiots" who can't replicate their method even when it is described to them in detail, or the research is flawed and overblown and not as effective as claimed. This seems like exactly the s…
> Why do a non-zero amount of people have seemingly religious beliefs about this topic on one side or the other? Because lots of engineers are being told by managers "Why aren't we using that tool?" and a bunch of engineers are stuck saying "Because it doesn't actually work." aka "Google is lying through their teeth." to which the response is "Oh, so you know better than Google?" to which the reponse is "Yeah, actual…
If you have personal experience with Jeff Dean et al that you're willing to share, I'd be interested in hearing about it.
From where I'm sitting it looks like, "Google spent a fortune on deep learning, and got a small but real win. People who don't like Google failed to follow Google's recipe and got a large and easily replicated loss."
It's not even clear that Google's approach is feasible right now for companies not named Google. It is not clear that it works on other classes of chip. It is not clear that the technique will grow beyond what Google already got. It is really not clear that anyone should be jumping on this.
But there is a world of difference between that, and concluding that Google is lying.
I have published an addendum to an article I wrote about AlphaChip ( https://vighneshiyer.com/misc/ml-for-placement/ ) at the very bottom that addresses this rebuttal from Google and the AlphaChip algorithm in general. In 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 ma…
A GPU costs $1-2/hr on the cloud market. So, ~$100-200 for inference, and ~$800-1600 for pre-training, which amortizes across chips. Cloud prices are an upper bound -- most CS labs will have way more than this available on premises.
In an industry context, these costs are completely dwarfed by the rest of the chip design process. (For context, the licensing costs alone for most commercial EDA software are in the millions of dollars.)