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How AlphaChip transformed computer chip design

deepmind.google

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Re: How AlphaChip transformed computer chip design

#51

Post by Workaccount2 »

vighneshiyer wrote: post #41

This work from Google (original Nature paper: https://www.nature.com/articles/s41586-021-03544-w ) has been credibly criticized by several researchers in the EDA CAD discipline. These papers are of interest: - A rebuttal by a researcher within Google who wrote this at the same time as the "AlphaChip" work was going on ("Stronger Baselines for Evaluating Deep Reinforcement Learning in Chip Placement"): http://47.190.8…

To be fair, some of these criticisms are a few years old. Which normally would be fair game, but the progress in AI has been breakneck. Criticism of other AI tech from 2021 or 2022 are pretty dated today.

Re: How AlphaChip transformed computer chip design

#52

Post by dgacmu »

int0x29 wrote: post #20
xanderlewis wrote: post #12

Earlier quoted context omitted.

Nice. Do you offer API access for a monthly fee?

I'll need 7 5 gigawatt datacenters in the middle of major urban areas or we might lose the Bubble Sort race with the Chinese.

Surely you'll be able to reduce this by getting TSMC to build new fabs to construct your new Bubble Sort Processors (BSPs).

Re: How AlphaChip transformed computer chip design

#53

Post by FrustratedMonky »

So AI designing it's own chips. Now that is moving towards exponential growth. Like at the end of "Colossus" the movie.

Forget LLM's. What DeepMind is doing seems more like how an AI will rule, in the world. Building real world models, and applying game logic like winning.

LLM's will just be the text/voice interface to what DeepMind is building.

Re: How AlphaChip transformed computer chip design

#54

Post by jeffbee »

Workaccount2 wrote: post #51
vighneshiyer wrote: post #41

This work from Google (original Nature paper: https://www.nature.com/articles/s41586-021-03544-w ) has been credibly criticized by several researchers in the EDA CAD discipline. These papers are of interest: - A rebuttal by a researcher within Google who wrote this at the same time as the "AlphaChip" work was going on ("Stronger Baselines for Evaluating Deep Reinforcement Learning in Chip Placement"): http://47.190.8…

To be fair, some of these criticisms are a few years old. Which normally would be fair game, but the progress in AI has been breakneck. Criticism of other AI tech from 2021 or 2022 are pretty dated today.

It certainly looks like the criticism at the end of the rebuttal that DeepMind has abandoned their EDA efforts is a bit stale in this context.

Re: How AlphaChip transformed computer chip design

#55

Post by epistasis »

lordswork wrote: post #44
epistasis wrote: post #6

Earlier quoted context omitted.

Google is good at many things, but perhaps their strongest skill is media positioning.

The media hates Google.

It a love/hate relationship. Which benefits Google and the media greatly.

Re: How AlphaChip transformed computer chip design

#56

Post by porphyra »

vighneshiyer wrote: post #41

This work from Google (original Nature paper: https://www.nature.com/articles/s41586-021-03544-w ) has been credibly criticized by several researchers in the EDA CAD discipline. These papers are of interest: - A rebuttal by a researcher within Google who wrote this at the same time as the "AlphaChip" work was going on ("Stronger Baselines for Evaluating Deep Reinforcement Learning in Chip Placement"): http://47.190.8…

The Deepmind chess paper was also criticized for unfair evaluation, as they were using an older version of Stockfish for comparison. Apparently, the gap between AlphaZero and that old version of Stockfish (about 50 elo iirc) was about the same as the gap between consecutive versions of Stockfish.

Re: How AlphaChip transformed computer chip design

#57

Post by sroussey »

ilaksh wrote: post #22

How far are we from memory-based computing going from research into competitive products? I get the impression that we are already well passed the point where it makes sense to invest very aggressively to scale up experiments with things like memristors. Because they are talking about how many new nuclear reactors they are going to need just for the AI datacenters.

The problem is that the competition (our current von neumann architecture) has billions of dollars of R&D per year invested.

Better architectures without the yearly investment train will no longer be better quite quickly.

You would need to be 100x to 1000x better in order to pull the investment train onto your tracks.

Don’t has been impossible for decades.

Even so, I think we will see such a change in my lifetime.

AI could be that use case that has a strong enough demand pull to make it happen.

We will see.

Re: How AlphaChip transformed computer chip design

#58

Post by tdullien »

amelius wrote: post #25
dreamcompiler wrote: post #13

Looks like this is only about placement. I wonder if it can be applied to routing?

Exactly what I was thinking. Also: when is this coming to KiCad? :) PS: It would also be nice to apply a similar algorithm to graph drawing (e.g. trying to optimize for human readability instead of electrical performance).

The issue is that in order to optimize for human readability you'll need a huge number of human evaluations of graphs?

Re: How AlphaChip transformed computer chip design

#60

Post by sroussey »

dharma1 wrote: post #40
colesantiago wrote: post #24

A marvellous achievement from DeepMind as usual, I am quite surprised that Google acquired them for a significant discount of $400M, when I would have expected it to be in the range of $20BN, but then again Deepmind wasn’t making any money back then.

it was very early. probably one of their all time best acquisitions in addition to YouTube. Re:using RL and other types AI assistance for chip design, Nvidia and others are doing this too

Applied Semantics for $100m which gave them their advertising business seems like their best deal.
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