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Comment by yeahwhatever10 | Hacker News Reader

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

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My state-of-art bubblesort implementation is also superhuman at sorting numbers.

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 a 1.21-GW datacenter would suffice!

Have we decided when are we deprecating it? I'm already cultivating another team in a remote location to work on a competing product that we will include into Google Cloud a month before deprecating this one.

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

I'll give you US$7Tn in investment. Just don't ask where it's coming from.

Nice. Still true though! We are in the bubble sort era of AI.

When we get better quantum computers we can start using spaghetti sort.

This is floorplanning the blocks, not every feature. We are talking dozens to hundreds of blocks, not billions-trillions of gates and wires.

I assume that the human benchmark is a human using existing EDA tools, not a guy with a pocket protector and a roll of tape.

Floorplanning algorithms and solvers already exist https://limsk.ece.gatech.edu/course/ece6133/slides/floorplan...

The original paper from DeepMind evaluates what they are now calling AlphaChip versus existing optimizers, including simulated annealing. They conclude that AlphaChip outperforms them with much less compute and real time.

https://www.cl.cam.ac.uk/~ey204/teaching/ACS/R244_2021_2022/...

Randomized algorithms strike again!

This is moreso amortized optimization/reinforcement learning, not randomized algorithms.

> They conclude that AlphaChip outperforms them with much less compute and real time.

Of course they do. I'm waiting for their products.

Believe it or not, but there was a time where algorithms were worse than humans at layout out transistors. In particular at the higher level design decisions.

That’s somewhat still the case, humans could do a much better job at efficient layouting. The problem is that humans don’t scale as well, laying out billions of transistors is hard for humans. But computers can do it if you forego some efficiency by switching to standard cells and then throw compute at the problem.

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

I feel like they're particularly bad at this, especially compared to other large companies.

Familiarity breeds contempt. They've been pushing the Google==Superhuman thing since the Internet Boom with declining efficacy.

The media hates Google.

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

I read the paper. Superhuman is a metric they defined in the paper which has to do with how long it takes a human to do certain tasks.

Does this make any sense, really? - Define some common words and then let the media run wild with them. How about we redefine "better" and "revolutionize"? Oh, wait, I think people are doing that already...

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?

"superhuman or comparable"

What nonsense! XD

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