[reitanqild]

> 10% here and there is very small compared to the literal orders magnitude improvements during the reign of Moore's Law.

I can't confirm it, but I noticed this comment says "gpu tech has beat Moore’s law for DNNs the last several years":

https://news.ycombinator.com/item?id=35653231

[marginalia_nu]

We're actually at an inflection point where this isn't the case anymore.

For a long time, GPU hardware basically became more powerful with each generation, but prices stayed roughly the same plus minus inflation. Last couple of years, this trend has broken. You pay double or even quadruple the price for a relatively tenuous increase in performance.

[Damogran6]

We said that in 1982, and 1987, and 1993, and 1995, and 2001, 2003, 2003.5

You get the point.

There's always local optimization that leads to improvements. Look at the Apple M1 chip rollout as a prime example of that. Big/Little processors, on die RAM, shared memory with the GPU and Neural Engine, power integration with the OS.

LOTS of things that led to a big leap forward.

[marginalia_nu]

Big difference now is that we have a clear inflection point. Die processes aren't getting much smaller than they are. A sub-nanometer process would involve arranging single digit counts of atoms into a transistor. A sub-Å process would involve single atom transistors. A sub 0.5Å process would mean making them out of subatomic particles. This isn't even possible in sci-fi.

You can re-arrange them for minor boosts, double the performance a few times sure, but that's not a sustained improvement month upon month like we have in the past.

As anyone who has ever optimized code will attest, optimization within fixed constraints typically hits diminishing returns very quickly. You have to work harder and harder for every win, and the wins get smaller and smaller.

[ben_w]

Current process nodes are mostly 5nm, with 3nm getting rolled out. Atomic is ~0.1nm, which is x30 linear and x900 by area.

However, none of that is actually important when the thing people care about most right now is energy consumed per operation.

This metric dominates for anything battery powered for obvious reasons; less obvious to most is that it's also important for data centres where all the components need to be spread out so the air con can keep them from being damaged by their own heat.

I've noticed a few times where people have made unflattering comparisons between AI and cryptocurrency. One of the few that I would agree with is the power requirements are basically "as much as you can".

Because of that:

> double the performance a few times sure, but that's not a sustained improvement month upon month like we have in the past.

"Doubling a few times" is still huge, even if energy efficiency was perfectly tied to feature size.

But as I said before, the maximum limit for energy efficiency is in the order of a billion-fold, not the x900 limit in areal density, and even our own brains (which have the extra cost of being made of living cells that need to stay that way) are an existential proof it's possible to be tens of thousands of times more energy efficient.

[kolinko]

That's not true. You can buy Raspberry PI, which is 10x cheaper and 10x more powerful than the computers at the beginning of 2000s.

Ditto with mobile phones. iPhone may be more expensive than when it launched, but you can buy dirt-cheap chinese smartphones that have similar performance - if not higher to the first iPhones.

[marginalia_nu]

I don't think this contradicts what I'm saying. This is happening now. Not 15 years ago.

[gg82]

Is that because of things unrelated to normal operations? From crypto coins, covid and now AI. I guess we may have to wait and see