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Comment by pfisherman | Hacker News Reader

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 mobile phone camera or automobile.

So again what does “better” mean in the context of this particular problem / task.

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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 approach were really revolutionary they would be selling the technology, not wasting their time writing blog posts about it.

> Floorplanning/placement/synthesis is a billion dollar industry

Maybe all together, but I don't think automatic placement algorithms are a billion dollar industry. There's so much more to it than that.

Yes in combination. Customers generally buy these tools as a package deal. If the placer/floorplanner blows everything else out of the water, then a CAD vendor can upsell a lot of related tools.

The original paper reports P&R metrics (WNS, TNS, area, power, wirelength, horizontal congestion, vertical congestion) - https://www.nature.com/articles/s41586-021-03544-w

(no paywall): https://www.cl.cam.ac.uk/~ey204/teaching/ACS/R244_2021_2022/...

From what I saw in the rebuttal papers, the Google cost-function is wirelength based. You can still get good TNS from that if your timing is very simplistic -- or if you choose your benchmark carefully.

They optimize using a fast heuristic based on wirelength, congestion, and density, but they evaluate with full P&R. It is definitely interesting that they get good timing without explicitly including it in their reward function!

Yeah; it means the heuristic they use is a good one

Interesting == Suspicious? I think this is a big red flag to those in the know.

The odd thing is that they don't compute timing in RL, but claim that somehow TNS and WNS improved. Does anyone believe this? With five circuits and three wins, the results are a coin toss.

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.

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

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.

More people see the blog posts as it’s a more gentle introduction than the paper itself. Sure it might generate interest in Google but it also generates interest for people to further look into the research. They are not for sales of the tech but I’m not sure the impact is just a recruitment tool even if that’s how Google justified the work to itself.

This is just floorplanning, which is a problem with fairly well defined quality metrics (max speed and chip area used).

Oh man, if only it were that simple. A floorplanner has to guestimate what the P&R tools are going to do with the initial layout. That can be very hard to predict -- even if the floorplanner and P&R tool are from the same vendor.

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