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Jeff Dean responds to EDA industry about AlphaChip

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Re: Jeff Dean responds to EDA industry about AlphaChip

#91

Post by gabegobblegoldi »

oesa wrote: post #86

In the tweet Jeff Dean says that Cheng at al. failed to follow the steps required to replicate the work of the Google researchers. Specifically: > In particular the authors did no pre-training (despite pre-training being mentioned 37 times in our Nature article), robbing our learning-based method of its ability to learn from other chip designs But in the Circuit Training Google repo[1] they specifically say: > Our re…

Markov’s paper also has links to Google papers from two different sets of authors that shows minimal advantage of pretraining. And given the small number of benchmarks using a pretrained model from Google whose provenance is not known would be counterproductive. Google likely trained it on all available benchmarks to regurgitate the best solutions of commercial tools.

Re: Jeff Dean responds to EDA industry about AlphaChip

#93

Post by nemonemo »

vighneshiyer wrote: post #87
nemonemo wrote: post #83

Earlier quoted context omitted.

Thank you for your thoughtful response. Acknowledging potential biases openly in a public forum is never easy, and in my view, it adds credibility to your words compared to leaving such matters as implicit insinuations. That said, on page 8, the paper says that 'standard licensing agreements with commercial vendors prohibit public comparison with their offerings.' Given this inherent limitation, what alternative appr…

So I'm not sure what Google is referring to here. As you can see in the ISPD paper ( https://vlsicad.ucsd.edu/Publications/Conferences/396/c396.p... ) on page 5, they openly compare Cadence CMP with AutoDMP and other algorithims quantitatively. The only obfuscation is with the proprietary GF12 technology, where they can't provide absolute numbers, but only relative ones. Comparison against commercial tools is actuall…

The UCSD paper says "We thank ... colleagues at Cadence and Synopsys for policy changes that permit our methods and results to be reproducible and sharable in the open, toward advancement of research in the field." This suggests that there may have been policies restricting publication prior to this work. It would be intriguing to see if future research on AlphaChip could receive a similar endorsement or support from these EDA companies.

Re: Jeff Dean responds to EDA industry about AlphaChip

#94

Post by jart »

_cs2017_ wrote: post #59

Curious why there's so much emotion and unpleasantness in this dispute? How did it evolve from the boring academic argument about benchmarks, significance, etc to a battle of personal attacks?

If you think this is unpleasant, you should see the environmentalists who try to take a poke at Jeff Dean on Twitter.

Re: Jeff Dean responds to EDA industry about AlphaChip

#95

Post by RicoElectrico »

_cs2017_ wrote: post #59

Curious why there's so much emotion and unpleasantness in this dispute? How did it evolve from the boring academic argument about benchmarks, significance, etc to a battle of personal attacks?

Making extraordinary claims without a way to replicate it. And then running to the press, which will swallow anything. Because "AI designs AI... umm... I mean chips" sounds futuristic to a liberal-arts majors (and apparently programmers too, which I'd expect to know better and question everything "AI")

The whole publication process seems dishonest, starting from publishing in Nature (why not ISCCC or something similar?)

Re: Jeff Dean responds to EDA industry about AlphaChip

#96

Post by menaerus »

radq wrote: post #54
bushbaba wrote: post #53

Earlier quoted context omitted.

h100 GPU instances are multiple orders of magnitude more expensive.

Not true, H100s cost $2-3/GPU/hr on the open market.

Yes, they even do at $1/GPU/hr. However, 8xH100 cluster at full utilization is ~8kWh of electricity and costs almost ~0.5M$. 16xH100 cluster is probably 2x of that. How many years before you break-even at ~24$/GPU/day income?

Re: Jeff Dean responds to EDA industry about AlphaChip

#97

Post by octoberfranklin »

bsder wrote: post #68
throwaway2037 wrote: post #63

Earlier quoted context omitted.

> EDA companies are garbage I don't understand this comment. Can you please explain? Are they unethical? Or do they write poor software?

Yes and yes. EDA companies are gatekeeping monopolies. They absolutely abuse their monopoly position to extract huge chunks of money out of companies, and are pretty much single-handedly responsible for the fact that the hardware startup ecosystem is moribund compared to that of the software startup ecosystem. They have been horrible liars about performance and benchmarketing for decades. They dragged their feet mise…

and are pretty much single-handedly responsible for the fact that the hardware startup ecosystem is moribund

Yes but not single-handedly -- it's them and the foundries, hand-in-hand.

No startup can compete with Synopsys because TSMC doesn't give out the true design rules to anybody smaller than Apple for finfet processes. Essentially their DRC+LVS software has become a DRM-encoded version of the design rule manual.

Re: Jeff Dean responds to EDA industry about AlphaChip

#98

Post by Jabbles »

vighneshiyer wrote: post #67
wholehog wrote: post #50

Earlier quoted context omitted.

We're talking 16 GPUs for ~6 hrs for inference, and 48 hrs for pre-training. This is not an exorbitant amount of compute. 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…

You are correct. For commercial use, the GPUs used for training and fine-tuning aren't a problem financially. However, if we wanted to rigorously benchmark AlphaChip against simulated annealing or other floorplanning algorithms, we have to afford the same compute and runtime budget to each algorithm. With 16 GPUs running for 6 hours, you could explore a huge placement space using any algorithm, and it isn't clear if…

You're saying that if the other methods were given the equivalent amount of compute they might be able to perform as well as AlphaChip? Or at least that the comparison would be fairer?

Are the other methods scalable in that way?

Re: Jeff Dean responds to EDA industry about AlphaChip

#99

Post by phonon »

jeffbee wrote: post #80
phonon wrote: post #51

Earlier quoted context omitted.

No it's not. They ran it longer instead.

The 2022 paper pretty explicitly says that runtime is not a substitute. They say their best result "can only be achieved in our 8-GPU setup".

I assume you mean Fig. 6 here?[0]

But that was explicitly limited to 8 hours for all setups. Do they have another paper that shows that you can't increase the number of hours of a smaller GPU setup to compensate?

[0]https://dl.acm.org/doi/pdf/10.1145/3505170.3511478

Re: Jeff Dean responds to EDA industry about AlphaChip

#100

Post by Jabbles »

menaerus wrote: post #96
radq wrote: post #54

Earlier quoted context omitted.

Not true, H100s cost $2-3/GPU/hr on the open market.

Yes, they even do at $1/GPU/hr. However, 8xH100 cluster at full utilization is ~8kWh of electricity and costs almost ~0.5M$. 16xH100 cluster is probably 2x of that. How many years before you break-even at ~24$/GPU/day income?

7

https://www.google.com/search?q=0.5e6%2F8%2F24%2F365

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