[GrumpyYoungMan]

The fabs propped up the corpse of Moore's Law by throwing mountains of cash at expanding transistors into the third dimension: finFET, GAA, CFET, etc. That has kept the party going a little while longer than it would have lasted but it's a one-time deal since are no more dimensions to expand into.

[brookst]

…but that’s how it’s always worked. Moore’a law is dead, we’re at the limit of everything, oh hey, Moore’s lawn limps by again because someone did something clever.

[light_hue_1]

That's never how it worked. Moore's law was never dead. People are just endlessly confused about what Moore's law is.

What ended was Dennard scaling around 2006. Roughly that frequency would keep going up as feature size went down. But because so many people are confused about what is what, you see a crappy muddled message.

Moore's law has been going strong. It must end eventually, current predictions are that it will be in a decade or two.

[perching_aix]

It's starting to get a bit old that whenever I see Moore's law mentioned, I'll usually also run into a spiel about how people have the wrong idea about what it actually refers to, and that it's holding up just fine. This is despite the gen-on-gen and year-on-year performance improvements of computer hardware very clearly tapering off in recent memory.

Maybe debating what always-somehow-wrong law to cite should not be the focus? Like it's very clear to me that being technically correct about what Moore's law or the Dennard scaling refers to is leaps and bounds less important than the actual, practical computing performance trends that have been observable in the market.

[Kon5ole]

What we see in the market is caused by software bloat. Chips are gaining performance faster than ever in absolute terms.

I think Moore’s law should be avoided altogether when discussing progress in this area, because it’s hard to understand the effects of doubling intuitively. Rice grains on chessboards and all that.

One might think ”Moore’s law is slowing down” means progress was faster before and slower now, when it is in fact completely opposite.

If you consider the 20 years between the intel 286 and the pentium 3, transistor count went from about 150 thousand to 10 million.

Today (using the ryzen 5950 and 7950 as examples), we got 5 Billion more transistors in just 2 years.

So in 2 years we added 500 times more transistors to our cpus than the first 20 years of “prime Moore’s law” did.

This enormous acceleration of progress is increasingly unnoticed due to even faster increases in software bloat, and the fact that most users aren’t doing things with their computers where they can notice any improvements in performance.

[perching_aix]

> Chips are gaining performance faster than ever in absolute terms.

But this is not what I as a consumer end up seeing at all. Consider the RTX 5090. Gen-on-gen (so, compared to the 4090), for 20-30% more money, using 20-30% more power, you get 20-30% more raster performance. Meaning the generational improvement is 0, software nonwithstanding.

> If you consider the 20 years between the intel 286 and the pentium 3, transistor count went from about 150 thousand to 10 million. Today (using the ryzen 5950 and 7950 as examples), we got 5 Billion more transistors in just 2 years.

Why would you bring absolute values into comparison with a relative value? Why compare the 286 and the P3 and span 20 years when you can match the 2 year timespan of your Ryzen comparison, and pit the P2 ('97) against the P3 ('99) instead? Mind you, that would reveal a generational improvement of 7.5M -> 28M transistors, a relative difference of +273%! Those Ryzens went from 8.3B to 13.2B, a +59% difference. But even this is misleading, because we're not considering die area or any other parameter.

[Kon5ole]

>But this is not what I as a consumer end up seeing at all. Consider the RTX 5090. Gen-on-gen (so, compared to the 4090), for 20-30% more money, using 20-30% more power, you get 20-30% more raster performance. Meaning the generational improvement is 0, software nonwithstanding.

The 4090 and 5090 are the same generation in reality, using the same process node. The 5090 is a bit larger but only has about 20% more transistors of the same type compared to the 4090. Which of course explains the modest performance boosts.

Nvidia could have made the 5090 on a more advanced node but they are in a market position where they can keep making the best products on an older (cheaper) node this time.

>Why would you bring absolute values into comparison with a relative value? Why compare the 286 and the P3 and span 20 years when you can match the 2 year timespan of your Ryzen comparison, and pit the P2 ('97) against the P3 ('99) instead? Mind you, that would reveal a generational improvement of 7.5M -> 28M transistors, a relative difference of +273%!

That was my point though, to highlight how relative differences in percentages represent vastly different actual performance jumps. It quickly becomes meaningless since it's not the percentages that matter, it is the actual number of transistors.

To put it another way - If you take the first pentium with about 3 million transistors as a baseline, you can express performance increases in "how many pentiums are we adding" instead of using percentages, and note that we are adding orders of magnitude more "pentiums of performance" per generation now than we did 10 years ago.

[relaxing]

Moore’s law never mentioned die area, and not because Gordon thought die sizes would never grow.

[Ekaros]

Take density per mm^2 going from 122.9 to 125.3 that is 2% increase in little over 2 years. Which does not bode well for needing to double in same period.

[_0ffh]

> This enormous acceleration of progress is increasingly unnoticed due to even faster increases in software bloat

Can't resist to throw in this quote (from one of Wirth's students I think):

"Software gets slower faster than hardware gets faster."

[brookst]

The spirit of Moore’s law is alive and well. It’s just that it doesn’t cover the whole story; it ignores efficiency, because that wasn’t a huge concern back then.

Sure, it’s not literally true of literal transistor density, but it feels arrogant to invent new names for the overall phenomenon that Moore observed. Like the way we still talk about Newton’s laws despite very valid “actually technically” incompleteness.

[sitkack]

It is ultimately a market effect, the technical specifics are not really important and are even conflated by industry insiders. See my sibling comment.

[wtallis]

Also, chip fabs keep getting more expensive and taping out a chip design for those new fabs keeps getting more expensive. That makes today's semiconductor industry work quite differently from how things worked 30 years ago and means some segments see reduced or delayed benefits from the continued progression of Moore's Law. See eg. the surprisingly long-lasting commercial relevance of 28nm, 14nm, and 7nm nodes, all of which were used in leading products like desktop GPUs and CPUs for more years than Moore's Law would lead you to expect.

[sitkack]

Moore's law ends when the whole universe is a computer (which it already is).

https://hasler.ece.gatech.edu/Published_papers/Technology_ov...

Some view it as a doubling every 18 months, or a cost per transistor (this has gone up with the smallest nodes).

It is roughly an exponential curve in the number of transistors we can use to make a "thing" with.

It is both a capability (can we make things of a certain number of transistors) and is it economically viable to build things of that size.

You could stay at the current node size and halve the cost of that wafer every 18 months and you would still be on the same curve. But it is easier in a our economic system to decrease the node size, keeping the rest of the fixed wafer costs the same and get 2x or 4x the density on the same lines.

If I get nerd sniped, I'd find the two video presentations one by Krste and another by Jim Keller where they unambiguously explain Dennard Scaling and Moore's Law in a way that is congruent with what I just said.

[eru]

> Moore's law ends when the whole universe is a computer (which it already is).

I find "Moore's Second Law" interesting. At least the version I'm familiar with says that the cost of a semiconductor chip fabrication plant doubles every four years. See https://en.wikipedia.org/wiki/Moore%27s_second_law

It's interesting to contrast that trajectory with global GDP. At some point, either global economic growth has to accelerate dramatically to even produce one fab; or we have to leave the 'globe', ie we go into space (but that's still universal GDP exploding), or that law has to break down.

It would be exceedingly funny (to me), if the one of the first two possibilities held true, and would accurately predict either an AI singularity or some Golden space age.

[DrNosferatu]

Moore’s will only end when we have practical optical computing, as it fills a critical technological niche.

It’s not impossible, but think I think quantum computing will only become practical later than optical.

[ForOldHack]

This is the kind of comment that will keep me laughing for weeks. Moore's lawn is in fact dead. We need to go back to calling what Moore said as his observational insight.

[dehrmann]

> into the third dimension

Does this actually work? At some point, and this is been the case for a while, you're limited by thermals. You can't stack more layers without adding more cooling.

[magicalhippo]

He's talking about how they've moved from planar transistors, where layers are just deposited on top of each other, to transisors with increasingly complex 3D structures[1] such as FinFET and Gate-All-Around, the latter having multiple nanowires passing through the gate like an ordered marble cake[2].

[1]: https://www.asml.com/en/news/stories/2022/what-is-a-gate-all...

[2]: https://anysilicon.com/the-ultimate-guide-to-gate-all-around...

[ForOldHack]

There are also cooling and conduction paths taken into account. It was discussed in the design of the xeon version of the i9. Which had me consider clocking down the performance core communication while throttling up the performance cores.

Your sources are excellent. ( Thank you so much for the links. )

[eru]

Moore's law doesn't say anything about you having to power all your transistors for them to count.

I'm only half-joking: the brain gets a lot of its energy efficiency out of most of its parts not working all that hard most of the time; and we are seeing some glimpses of that in mobile processors, too.

[WXLCKNO]

Depends, there could be 11 dimensions to expand into.

[skissane]

Assuming those extra dimensions really exist (it is unproven), I think we are centuries or even millennia away from being able to make technological use of them-if we ever will be at all

[relaxing]

Expand into the time dimension, evaluate infinite execution paths by reversing the pipeline, rerunning, and storing the results in a future accumulator.

[ForOldHack]

You give a whole new meaning to branch prediction. I knew you were going there, and I still did not avoid the brain twang.

[acchow]

> since are no more dimensions to expand into.

Quantum computing is next, right?

[adastra22]

That’s not how quantum computing works.

[eru]

To elaborate:

Apart from the very niche application of factoring integers into prime numbers, there's scarcely any application know where quantum computers would even theoretically outperform classical computers. And even integer factoring is only remotely useful, until people completely switch away from cryptography that's relies on it.

The one useful application of quantum computing that I know of is: simulating quantum systems. That's less useless than it sounds: a quantum computer can simulate not just itself (trivially), but also other quantum systems. In any case, the real world use case for that is for accelerating progress in material science, not something you nor me would use everyday.

[adastra22]

This isn’t an elaboration on anything I said. Quantum computers are immensely useful across a whole slew of domains. Not just cryptanalysis, but also secure encryption links, chemistry simulations, weather predictions, machine learning, search, finance, logistics, classical simulations (e.g. fluid flow) and basically anywhere you have linear algebra or NP problems.

[eru]

Do you have any sources that give good evidence that quantum computers are useful for 'weather predictions, machine learning, search, finance, logistics, classical simulations (e.g. fluid flow) and basically anywhere you have linear algebra or NP problems'? I'm basing my skepticism mostly on the likes of Scoot Aaronson.

I can believe that quantum computers might be useful for chemistry simulations (Quantum computers aren't really useful for encryption. But you could theoretically use them. They just don't really give you any advantage over running a quantum resistant algorithm on a classic computer.)

I'm especially doubtful that quantum computer would be useful for arbitrary NP problems or even arbitrary linear algebra problems.

[adastra22]

Both answers here are a good start:

https://cs.stackexchange.com/questions/76525/could-a-quantum...

There are specialized algorithms for any part of it, especially search. But demonstrating that quantum computers are good for linear algebra should be enough to show that they are generally useful, I hope.

The encryption I was referring to was quantum link encryption (technically not a quantum computer, but we are splitting hairs here; it uses the same set of underlying mechanisms). Quantum link encryption permits you to have a communications channel that if someone tries to man in the middle, all it does is break the link. Both you and the attacker only see gibberish. It’s like a one time pad that doesn’t require first exchanging pads.