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How AlphaChip transformed computer chip design

deepmind.google

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Re: How AlphaChip transformed computer chip design

#121

Post by foota »

ilaksh wrote: post #105
foota wrote: post #104

Earlier quoted context omitted.

I don't think it's ignorant to not invest in something that has a decade long path towards even having a market, much less a large market.

I have seen at least one experiment running a language model or other neural network on (small scale) memory-based computing substrates. That suggests less than 1-2 years to apply them immediately to existing tasks once they are scaled up in terms of compute capacity.

I would have assumed it would take many years longer than that to scale something like this up, based on how long it takes traditional CPU manufacturers to design state of the art chips and manufacturing processes.

Re: How AlphaChip transformed computer chip design

#122

Post by AshamedCaptain »

negativeonehalf wrote: post #76
yeahwhatever10 wrote: post #5

Why do they keep saying "superhuman"? Algorithms are used for these tasks, humans aren't laying out trillions of transistors by hand.

Prior to AlphaChip, macro placement was done manually by human engineers in any production setting. Prior algorithmic methods especially struggled to manage congestion, resulting in chips that weren't manufacturable.

> macro placement was done manually by human engineers in any production setting

To quote certain popular TV series .... Sorry, are you from the past? Do your "production" chips only have a couple dozen macros or what?

Re: How AlphaChip transformed computer chip design

#123

Post by AshamedCaptain »

loandbehold wrote: post #28

Every generation of chips is used to design next generation. That seems to be the root of exponential growth in Moore's law.

I'm only tangential to the area, but my impression over the decades is that what is going to happen is that, eventually, designing the next generation is going to require more resources than the current generation can provide, thereby putting a hard stop at the exponential growth stage.

I'd even dare to claim we are already at the point where the growth has stopped, but even then you will only see the effect in a decade or so as there are still many small low-hanging fruits you can fix, but no big improvements.

Re: How AlphaChip transformed computer chip design

#124

Post by wegfawefgawefg »

negativeonehalf wrote: post #71
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…

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.

Re: How AlphaChip transformed computer chip design

#125

Post by therealcamino »

sroussey wrote: post #57
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…

If you don't worry about the programming model, it's pretty easy to be way better than than existing methodologies in terms of pure compute.

But if you do pay attention to the programming model, they're unusable. You'll see that dozens of these approaches have come and gone, because it's impossible to write software for them.

Re: How AlphaChip transformed computer chip design

#128

Post by rossjudson »

Drunk_Engineer wrote: post #73
pfisherman wrote: post #17

Questions for those in the know about chip design. How are they measuring the quality of a chip design? Does the metric that Google is reporting make sense? Or is it just something to make themselves look good? Without knowing much, my guess is that “quality” of a chip design is multifaceted and heavily dependent on the use case. That is the ideal chip for a data center would look very different from those for a mobi…

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.

Re: How AlphaChip transformed computer chip design

#129

Post by dsv3099i »

pelorat wrote: post #101
hulitu wrote: post #84

Earlier quoted context omitted.

They don't. You cannot compare reality (Cadence, Synopsys) with hype (Google).

So you're basically saying that Google should have used existing tools to layout their chip designs, instead of their ML solution, and that these existing tools would have produced even better chips than the ones they are actually manufacturing?

It’s more like no one outside of Google has been able to reproduce Google’s results. And not for lack of trying. So if you’re outside of Google, at this moment, it’s vapor.

Re: How AlphaChip transformed computer chip design

#130

Post by bankcust08385 »

Technology singularity is around the corner as soon as the chips (mostly) design themselves. There will be a few engineers, zillions of semiskilled maintenance people making a pittance, and most of the world will be underemployed or unemployed. Technical people better understand this and unionize or they will find themselves going the way of piano tuners and Russian physicists. Slow boiling frog...
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