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gabegobblegoldi | 1 year ago | on: Jeff Dean responds to EDA industry about AlphaChip

Looks like he aligned himself with the wrong folks here. He is a system builder at heart but not an expert in chip design or EDA. And also not really an ML researcher. Some would say he got taken for a ride by a young charismatic grifter and is now in too deep to back out. His focus on this project didn’t help with his case at Google. They moved all the important stuff away from him and gave it to Demis last year and left him with an honorary title. Quite sad really for someone of his accomplishments.

gabegobblegoldi | 1 year ago | on: Jeff Dean responds to EDA industry about AlphaChip

The court case provides more details. Looks like the junior researchers and Jeff Dean teamed up and bullied Chatterjee and his team to prevent the fraud from being exposed. IIRC the NYT reported at the time that Chatterjee was fired within an hour of disclosing that he was going to report Jeff Dean to the Alphabet Board for misconduct.

gabegobblegoldi | 1 year ago | on: Jeff Dean responds to EDA industry about AlphaChip

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.

gabegobblegoldi | 1 year ago | on: Jeff Dean responds to EDA industry about AlphaChip

As Markov claims Nature did not follow their own policy. Since Google’s results are only on their designs, no one can replicate them. Nature is single blind, so they probably didn’t want to turn down Jeff Dean so that they wouldn’t lose future business from Google.

gabegobblegoldi | 1 year ago | on: How AlphaChip transformed computer chip design

Good question. I thought the tpus were a way for Google to apply pricing pressure to nvidia by having an alternative. They are not particularly better (it’s hard to get utilization), and I believe Google continues to be a big buyer of nvidia chips.

gabegobblegoldi | 1 year ago | on: How AlphaChip transformed computer chip design

Sorry. I meant obscure relative to the large space of combinatorial optimization problems not just chip design.

Most design houses don’t write their own macro placers but customize commercial flows for their designs.

The problem with macro placement as an RL technology demonstrator is that to evaluate quality you need to go through large parts of the design flow which involves using other commercial tools. This makes it incredibly hard to evaluate superiority since all those steps and tools add noise.

Easier problems would have been to use RL to minimize the number of gates in a logic circuit or just focus on placement with half perimeter wirelength (I think this is what you mean with your grad student example). Essentially solving point problems in the design flow and evaluating quality improvements locally.

They evaluated quality globally and only globally and that destroys credibility in this business due to the noise involved unless you have lots of examples, can show statistical significance, and (unfortunately for the authors) also local improvements.

That’s what the follow on studies did and that’s why the community has lost faith in this particular algorithm.

gabegobblegoldi | 1 year ago | on: How AlphaChip transformed computer chip design

Doesn’t look like it. In fact the original paper claimed that their RL method could be used for all sorts of combinatorial optimization problems. Yet they chose an obscure problem in chip design and showed their results on proprietary data instead of standard public benchmarks.

Instead they could have demonstrated their amazing method on any number of standard NP hard optimization problems e.g. traveling salesman, bin packing, ILP, etc. where we can generate tons of examples and verify easily whether it produces better results than other solvers or not.

This is why many in the chip design and optimization community felt that the paper was suspicious. Even with this addendum they adamantly refuse to share any results that can be independently verified.

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