Roughly every two years, the density of transistors that can be fit onto a silicon chip doubles.
The complexity for minimum component costs has increased at a rate of roughly a factor of two per year.
Effectively, it was always more of a "marketing law" than an engineering one. Semiconductor chips only had 18-36 months to reap big profits, so Intel tried to stay ahead of that curve.
https://hopefullyintersting.blogspot.com/2019/03/what-is-moores-law.html
Dual ported TCAM memory isn't getting faster and we've got to 1,000,000 prefixes in the internet and ipv6 are 4 times bigger. Memory speed is a real issue.
Re garage invention: lithography is probably too big an issue for that. It's important to keep in mind that we're currently producing a lot of transistors with today's tech. Any alternative would have to match that (eg stamping technologies).
(I work on lithography optics)
Ultimately, there's a cap. For as far as I know, the universe is finite.
I don't think we know that. We don't even know how big the universe really is - we can only see so far. All we have is a best guess.
There may also be a multiverse out there (or right beside us).
And, creating universes might be a thing.
... I don't expect Moore's law to hold for ever either, but I don't believe in creating unnecessary caps.
As I understand it Moore's Law doesn't address any sort of fundamental physical limitations other than perhaps an absolutely limit in terms of some fundamental limit on the smallness of an object, it's just an observation of the doubling of transistor density over a consistent period of time.
It seems more like an economical or social observation than a physical one to me.
So we may have Apple and NVidia as the only ones that can afford to build a fab. Edit, correction, Microsoft is the current number 2 company by market cap.
That's... not how this works at all. Eventually the depletion region where the positive or negative charge carriers (for p or n doped silicon) deplete far enough and then at the threshold voltage inversion happens when the opposite sort of charge carrier start to accumulate along the oxide and allow conduction. By surrounding the channel there's less space for a depletion region and so inversion happens at lower voltages, leading to higher performance. Same as people used to do with silicon on oxide.
The Wikipedia article has nice diagrams:
That means that the channels are inherently intrinsic. You can't really have doping when there's statistically less than an atom. There's a nice review from 2023.
https://semiengineering.com/what-designers-need-to-know-about-gaa/
https://www.semiconductor-digest.com/the-shape-of-tomorrows-semiconductor-technology/
This is more recent with pretty pictures.
The key is that all increases in transistor count are now based on either stacking layers in the silicon (like flash scaling), stacking die with chiplets (we're already at 12-16 die for HBM) or scaling beyond the reticle limit (there's a lot of investment in larger substrates right now). None of these help cost scaling and all have huge yield challenges.
Moore's law really ends when the investors and CFOs decide it does. The generative AI boom has extended that for a while.
that is, ML is about periodic shrinks producing squared improvements to device density at iso-area, and thus costs. if you have to change equipment, ML (in this narrow sense) is out the window. if you have to spend linear resources to stack die or chiplets, that's also not ML-compatible (because linear, not 1/(shrink^2))
vertical flash is interesting because it appears to scale surprisingly well - that is, extra layers don't seem to exponentially degrade yield. I'm not sure any of that applies to logic, though.
that... isn't the moore law, it is about count / complexity, not density. and larger chips are a valid way to fullfill it.
https://hasler.ece.gatech.edu/Published_papers/Technology_overview/gordon_moore_1965_article.pdf
https://www.eng.auburn.edu/~agrawvd/COURSE/E7770_Spr07/READ/Gordon_Moore_1975_Speech.pdf
> the density of transistors that can be fit onto a silicon chip doubles
the whole article takes off from a flawed and fantasious misinterpretation and argue against that self created windmill
This seems improbable.
50-year-old technology works because 50 years ago, transistors were micron-scale.
Nanometer-scale nodes wear out much more quickly. Modern GPUs have a rated lifespan in the 3-7 year range, depending on usage.
One of my concerns is we're reaching a point where the loss of a fab due to a crisis -- war, natural disaster, etc. -- may cause systemic collapse. You can plot lifespan of chips versus time to bring a new fab online. Those lines are just around the crossing point; modern electronics would start to fail before we could produce more.
I recently bought a new MacBook, my previous one having lasted me for over 10 years. The big thing that pushed me to finally upgrade wasn’t hardware (which as far as I could tell had no major issues), it was the fact that it couldn’t run latest macOS, and software support for the old version it could run was increasingly going away.
The battery and keyboard had been replaced, but (AFAIK) the logic board was still the original
which is very annoying, as none of the newer OS versions has anything that warrants dumping hardware to buy brand new to run them with! With the exception of security upgrades, which i find dubious for a company to stop creating (as they would need to do so for their newer OS versions just as well, so the cost of maintaining security patches ought to not be much, if at all), it is definitely more likely to be a dark-pattern to force hardware upgrades.
As long as there are enough users of some hardware, free software will support it, because the users of that hardware want it to.
Also he said that software from third parties also don’t support the older OS so even if Apple did provide security updates, he would still be in the same place.
The screen on the MacBookPro10,2 is 2560x1600 which is still higher than a lot of brand new laptops. The latest version it will run is 10.15 from 2019. I know Apple switched to ARM but most people don't need a new faster computer. I stopped buying Apple computers because I want my computer supported more than 6 years.
I do have 3 newer computers but these old Macbooks are kept at various relative's houses for when I visit and wnat my own Linux machine. They have no problems running a web browser and watching videos so why replace them?
That statement absolutely needs a source. Is "usage" 100% load 24/7? What is the failure rate after 7 years? Are the failures unrepairable, i.e. not just a broken fan?
By 'Modern' they must mean latest generation, so we'll have to wait and see. I was imagining not using an RTX 5090 for 7 years and find it doesn't work, or one used 24x7 for 3 years then failing.
The silicon gates in GPUs just don’t wear out like that, not at that timescale. The only thing that sort of does is SSDs (and that’s a write limit, which has existed for decades, not a new thing).
I seriously doubt this is true. The venerable GTX 1060 came out 9 years ago, and still sees fairly widespread use based on the Steam hardware survey. According to you, many (most?) of those cards should have given out years ago.
since then, there have been some adjustments, but it still holds as a prediction of a general trend since as noted in that article:
>One reason for the success of Moore’s prediction is that it became a guide — almost a target — for chip designers.
but as noted:
>The days when we could double the number of transistors on a chip every two years are far behind us. However, Moore’s Law has acted as a pacesetter in a decades-long race to create chips that perform more complicated tasks quicker, especially as our expectations for continual progress continue.
> How small or fast or efficient a transistor can be made in a lab is of absolutely no relevance if they can’t be mass-manufactured at a price anyone is willing to pay.
Clearly what is driving advances in compute nowadays is not single-chip transistor density but instead multi-chiplet datacenter processors and much higher level multi-chip connectivity such as TPU pods and AI datacenter designs.
1) exponentially decreasing cost per transistor and
2) exponentially decreasing power consumption per transistor.
However, in recent years 1) has generally weakened. At some point, the price per transistor will no longer decrease for smaller process nodes, and even start to increase. Then making smaller transistors could only be justified for power constrained chips that benefit from 2). But even this has only limited value.
So at some point, producing chips with smaller transistors will no longer make economic sense, even if transistor size could technically still be decreased at a similar rate as in the past.
As the article points out, Moore's Law was principally about the number of features. Today, the "Intel ISA" portion of one of their CPU cores is dwarfed by caches and vector & machine learning processing facilities devoted to that same core. Off-chip memory access and other I/O is still a huge factor for why we don't have 10 GHz processors yet. These days we are devoting silicon resources to slowing things down - due to previous performance designs that turned out to have flaws (SPECTRE, Meltdown, etc).
The features per nm just keep on a-coming.
The most persuasive argument is the one that he puts in the beginning; at the current rate, we are fast approaching the point where there are no companies left who can afford a new fab.
> We’re entering the post-Moore era, I’m busy designing chips (and maybe a fab) for this new world. I’d be happy to talk to investors.
We were told in uni in the early 2000s that post-Moore era was just few years from then.
Which did roughly end in the mid 2000s. That's why we've spent so much time parallelizing in the past 20 years rather than just expecting increases in single threaded perf
https://en.m.wikipedia.org/wiki/Moore%27s_law
It tells us more about market and ppl's hunger for apps/cabilities than fabrication and physics, although fabrication quality is indirectly related through cost.
I'm waiting somewhat impatiently for AtomicSemi to make some announcements.
especially after describing all the other desperate scaling techniques in the past - and that history argues for the need (almost manifest destiny in the ML sense) to do these things.
As interesting as this breakdown of the current state of things is, it doesn’t tell us much we didn’t know or predict much about the future, and that’s the thing I most wanted to hear from an expert article on the subject, even if we can take it with a large pinch of salt.
>software is getting slower more rapidly than hardware is becoming faster.
>Wirth attributed the saying to Martin Reiser, who in the preface to his book on the Oberon System wrote: "The hope is that the progress in hardware will cure all software ills. However, a critical observer may observe that software manages to outgrow hardware in size and sluggishness."
I wish that there would be more instances of developments like to Mac OS X 10.6, where rather than new features, the software was simply optimized for a given CPU architecture, and the focus was on improving performance.
We haven't been bound by moore's law because we just waste computing power because programmers are expensive. No one is trying to optimize nowadays except in very niche places. And when push comes to shove we just start wasting slightly less. Like adding a JIT to a script language 15 years too late.
Given the power and ubiquity of smart phones, most people don't need any other computer in their personal life. What can be done locally on a smart phone seems like it will be more constrained by battery life and physical size than anything else, and there will continue to be a mix of things that can run on-device and other more compute-hungry functions run in the cloud. I don't see smartphones being replaced or augmented by other devices like smart glasses - people want one device that does it all, and not one they wear on their face.
The same is somewhat true for business use too, especially if compute-heavy AI use becomes more widespread - some functions local and the heavy work done in AI-ready datacenters. I'm mildly surprised that there hasn't already been a greater shift away from local compute to things like Chromebooks (ubiquitous in schools) since it has so many benefits (cost, ease of management, reduced need to upgrade), but maybe it will still come if the need to rely on datacenter compute increases.
Even if we imagine futuristic power-sipping neuromorphic chips, I don't see that changing things very much other that increasing the scope of what can be done locally on a power budget.
For users, a few hundred dollar extra ( on top of the original purchase ) is a such a small number compared to the productivity gain over the usage span of the computer.
AI alone not only increased the server hardware requirement but also user client requirement. It is basically the question everyone has been asking, what is after Smartphone? And to a degree it is AI. ( or LLM )
This will easily push the whole Semi-Conductor Industry forward all the way to 2032 ~ 2035. We will be at 8A or 6A by then.
PCIe 7? possibly PCIe 8? WiFi 9 which is a fixed version of WiFi 8. There are so many great Hardware improvement coming out all because of the demand of greater computing usage.
Software side has been rather boring TBH. I really like the phase Allan Kay uses to describe modern days software are "reinventing the flat tire".