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How AlphaChip transformed computer chip design

deepmind.google

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Re: How AlphaChip transformed computer chip design

#151

Post by vlovich123 »

lacker wrote: post #134
porphyra wrote: post #56

Earlier quoted context omitted.

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.

Indeed, six years later, the AlphaZero algorithm is not the best performing algorithm for chess. LCZero (uses AlphaZero algorithm) won some TCECs after it came out but for the past few years Stockfish (does not use AlphaZero algorithm) has been winning consistently. https://en.wikipedia.org/wiki/Top_Chess_Engine_Championship So perhaps the critics had a point there.

There’s a lot of codevelopment happening in the space where the positions are evaluated by Leela and then used to train the NNUE net within stockfish. And Leela comes from AlphaZero. So basically AlphaZero was directly responsible for opening up new avenues of research for a more specialized chess engine to reach new levels than it could have without it.

> Generally considered to be the strongest GPU engine, it continues to provide open data which is essential for training our NNUE networks. They released version 0.31.1 of their engine a few weeks ago, check it out!

[1]

I’d say the impact AlphaZero has had on chess and go can’t be understated considering it’s a general algorithm that at worst is highly competitive with purpose built engines. And that’s ignoring the actual point of why DeepMind is doing any of this which is for GAI (that’s why they’re not constantly trying to compete with existing engines)

[1] https://lichess.org/@/StockfishNews/blog/stockfish-17-is-her...

Re: How AlphaChip transformed computer chip design

#152

Post by hanwenn »

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

don't forget Android.

Re: How AlphaChip transformed computer chip design

#153

Post by thenoblesunfish »

rossjudson wrote: post #128
Drunk_Engineer wrote: post #73

Earlier quoted context omitted.

I have not read the latest paper, but their previous work was really unclear about metrics being used. Researchers trying to replicate results had a hard time getting reliable details/benchmarks out of Google. Also, my recollection is that Google did not even compute timing, just wirelength and congestion; i.e. extremely primitive metrics. Floorplanning/placement/synthesis is a billion dollar industry, so if their ap…

Like when Google wasted its time writing publicly about Spanner? https://research.google/pubs/spanner-googles-globally-distri... or Bigtable? https://research.google/pubs/bigtable-a-distributed-storage-... or GFS? or MapReduce? or Borg? or...I think you get the idea.

I am not sure these publications were intended to generate sales of these technologies. My assumption is that they mostly help the company in terms of recruitment. This lets potential employees see cool stuff Google is doing, and see them as an industry leader.

Re: How AlphaChip transformed computer chip design

#154

Post by vlovich123 »

thenoblesunfish wrote: post #153
rossjudson wrote: post #128

Earlier quoted context omitted.

Like when Google wasted its time writing publicly about Spanner? https://research.google/pubs/spanner-googles-globally-distri... or Bigtable? https://research.google/pubs/bigtable-a-distributed-storage-... or GFS? or MapReduce? or Borg? or...I think you get the idea.

I am not sure these publications were intended to generate sales of these technologies. My assumption is that they mostly help the company in terms of recruitment. This lets potential employees see cool stuff Google is doing, and see them as an industry leader.

Spanner is literally a Google cloud product you can buy ignoring that it underpins a good amount of Google tech internally. The same is true of other stuff. Dismissing it as a recruitment tool indicates you haven’t worked at Google or really know much about their product lines.

Re: How AlphaChip transformed computer chip design

#155

Post by Kubuxu »

bombita wrote: post #139
bushbaba wrote: post #131

Earlier quoted context omitted.

Spanner research paper was in 2012. Bigtable was in 2006. GFS 2003. The last decade has been a 'lost decade' of google. Not much innovation to be honest.

Attention is all you need is 2017... https://arxiv.org/abs/1706.03762

They thought it was dead end, that is why they released it :P

Re: How AlphaChip transformed computer chip design

#156

Post by smokel »

clickwiseorange wrote: post #81
negativeonehalf wrote: post #71

Earlier quoted context omitted.

FD: I have been following this whole thing for a while, and know personally a number of the people involved. The AlphaChip authors address criticism in their addendum, and in a prior statement from the co-lead authors: https://www.nature.com/articles/s41586-024-08032-5 , https://www.annagoldie.com/home/statement - The 2023 ISPD paper didn't pre-train at all. This means no learning from experience, for a learning-base…

Oh, man... this is the same old stuff from the 2023 Anna Goldie statement (is this Anna Goldie's comment?). This was all addressed by Kahng in 2023 - no valid criticisms. Where do I start? Kahng's ISPD 2023 paper is not in dispute - no established experts objected to it. The Nature paper is in dispute. Dozens of experts objected to it: Kahng, Cheng, Markov, Madden, Lienig, Swartz objected publically. The fact that Ka…

Wow, you seem to be pretty invested in this topic. Care to clarify?

Re: How AlphaChip transformed computer chip design

#157

Post by 317070 »

wegfawefgawefg wrote: post #124
negativeonehalf wrote: post #71

Earlier quoted context omitted.

FD: I have been following this whole thing for a while, and know personally a number of the people involved. The AlphaChip authors address criticism in their addendum, and in a prior statement from the co-lead authors: https://www.nature.com/articles/s41586-024-08032-5 , https://www.annagoldie.com/home/statement - The 2023 ISPD paper didn't pre-train at all. This means no learning from experience, for a learning-base…

In reinforcement learning pre-training reduces peak performance. We can argue about this, but it is not a sufficiently strong point to stop reading from alone.

Do you have a citation for this? I did my Phd on this topic 8 years ago, and I didn't completely follow the field after. I'm curious to learn more.

Re: How AlphaChip transformed computer chip design

#158

Post by hinkley »

therealcamino wrote: post #113
hinkley wrote: post #14

TSMC made a point of calling out that their latest generation of software for automating chip design has features that allow you to select logic designs for TDP over raw speed. I think that’s our answer to keep Dennard scaling alive in spirit if not in body. Speed of light is still going to matter, so physical proximity of communicating components will always matter, but I wonder how many wins this will represent ver…

EDA software has long allowed trading off power, delay, and area during optimization . But TSMC doesn't produce those tools, as far as I'm aware.

https://www.tsmc.com/english/dedicatedFoundry/oip/eda_allian...

They don’t produce but they are tailored for them just the same. “We have” doesn’t have to mean “we made”. They don’t say it as such here but elsewhere they refer to the IP they can make available, which can also be made in house or cross licensed and still count as “we have”.

Re: How AlphaChip transformed computer chip design

#159

Post by bgnn »

smaddox wrote: post #75
bgnn wrote: post #65

Earlier quoted context omitted.

That's wrong. Chip design and Moore's law have nothing to do with each other.

To clarify what the parent is getting at: Moore's law is an observation about the density (and, really about the cost) of transistors. So it's about the fabrication process, not about the logic design. Practically speaking, though, maintaining Moore's law would have been economically prohibitive if circuit design and layout had not been automated.

That's true. The impact on design is reverse of the post I replied to though. Since we got more density, we had more compute available to automate more, which made it economically viable. Every generation had enough compute to design the next generation. Now the device scaling is stagnated, we have more (financially viable) compute available to us than before (compared to design complexity). This is why this AI generated floorplans become viable I think. I'm not sure if it would have been the same should the device scaling would be continuing at its peak.

I want to emphasize the biggest barrier for IC design to the outsiders: prohibitively expensive software licenses. IC design software costs are the much higher than conpute and the production costs, and often similar order of magnitude but definitely higher than engineer salaries. This is because of the monopoly of the 3 big companies (Synopsys, Cadence and Mentor Graphics). What wxcites me the most about stuff like OP isn't AI, everyone is doing that. It's the premise of more competition and even open source tool options. In the good old days companies used to have their im-house tools. They are all sacrificed (and pretty much none made open source) because investors thought it's not a core business, so it's inefficient. Now even Nvidia or Apple have no alternative.

Re: How AlphaChip transformed computer chip design

#160

Post by ebalit »

vlovich123 wrote: post #154
thenoblesunfish wrote: post #153

Earlier quoted context omitted.

I am not sure these publications were intended to generate sales of these technologies. My assumption is that they mostly help the company in terms of recruitment. This lets potential employees see cool stuff Google is doing, and see them as an industry leader.

Spanner is literally a Google cloud product you can buy ignoring that it underpins a good amount of Google tech internally. The same is true of other stuff. Dismissing it as a recruitment tool indicates you haven’t worked at Google or really know much about their product lines.

He didn't say that Spanner is only a recruitment tool but that the blog posts about Spanner (and other core technologies of Google) might be.
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