Intel Stockpiling 10nm Chips
arstechnica.com
arstechnica.com
(that's a tl;dr for https://www.semiaccurate.com/2019/04/25/leaked-roadmap-shows... )
If you had read the SemiAccurate article you would know that.
Ice Lake-U sounds better on paper - at the very least it's a 4C die.
Intel's first crack at 10nm was a jaw-dropping affair for how poorly it showed.
I'd say, a lot would've been fine even if Intel gave them existing 14nm chips, but since all 14nm capacity is gobbled by server chips, it's not gonna happen.
I believe Intel is making a huge error by prioritising big wholesale Xeon buyers like Amazon over OEM manufacturers. Hosting providers will be back the moment Intel regains the tech lead or finally makes 10nm Xeons, but OEMs will walk away and not return.
- If the big wholesale Xeon buyers and users qualify AMD chips and start buying a lot of them, Intel will find that landscape a lot more competitive, this is a more "sticky" market than OEM products for consumers.
- They could be betting that AMD will lose the plot again, as they've done throughout their corporate history, e.g. after the 486, and after K8. The OEM consumer market is less sticky and will trivially move back to Intel if they have competitive offerings.
In OEM, things are dominated by much more complex dynamics in between supply chain, factories, and clients.
Intel had no problem with Taiwanese, as a lot of TW companies had relationship with Intel since the "Desktop First" era, but in mainland, Intel had to fight a bloody battle for the fresh and juicy laptop and tablet markets for the last decade, with kickbacks, blackjack, and hookers. Their position here was never strong to begin with.
(1) https://www.cpubenchmark.net/compare/Intel-i5-9600K-vs-Intel...
Or everyone has to make a dramatic leap to a new underlying computing physics, like the quantum dot transistor or the vacuum transistor [1] that lets them increase frequency or lower power consumption. If you double the clock frequency, I believe this still linearly increases the computing throughput, though I'm not expert enough to know if that's still strictly true.
[1] https://spectrum.ieee.org/semiconductors/devices/introducing...
I'd kind of figure that such a technology would also speed up the RAM, but I suppose that's not true either if the underlying storage technology is too slow. But I know that's also an area of active research.
Your core might be small, but the industrial-sized refrigerator needed to cool it suddenly isn't.
A similar line of thinking lead to source-coupled logic being resurrected for use in high speed PHYs https://en.wikipedia.org/wiki/Current-mode_logic
A vacuum transistor, used as a switch, is still a switch.
And in general, the end of process size reduction doesn't mean the end of innovation in chip building and design. There are limits on node sizes, as there are limits on heat dissipation, but once you have high enough yield, there are few limits onto how many circuits you put onto a chip, as long as most of the circuits are powered off. Ultimately, the more stuff special purpose circuits compute instead of the general purpose one, the better for power consumption.
What is "risk production?" Is this similar to prototyping? I'm not familiar with this term. Thanks
In practical terms it means they aren't building low cost consumer products yet, they are probably building low volume high $ parts that desperately need 5nm. They are also probably doing a bunch of IP verification runs, confirming process designs for all the building blocks that are used to build SoCs: ARM cores, memory controllers, IO controllers, etc.
What sorts of parts fit this criteria? Details would be appreciated, thanks!
And to the point the OP was asking, I don't think there is a technical problem to 3nm, or even 2nm process size. Expect this year's iPhone A Series SoC to be on 7nm EUV, 5nm in 2020, 3nm in 2022, likely 2nm in 2024 / 2025. There may be a 5nm+ and 3nm+ in between those. Or as TSMC's CEO Joked, expect to see 5.5nm in the future.
We don't have a technical barrier, we do have an economical / cost barrier. With every node progression near to doubling of design cost, not everyone can afford to be on leading node. [1] The extremetech article is an easy to read version of Semi Engineering [2], if we assume the number are correct ( I do think the number are little exaggerated ) $500M for 5nm. Apple will need to find a $2.5 BOM reduction per iPhone to off set that cost. Qualcomm will have to sell their chip $5 to $10 more expensive. Now if you imagine $1B for 3nm, and $2B+ for 2nm.
Unless you are Apple, who can easily hike the price by another $100. For others like Qualcomm, this is going to create lots of problems unless they could spread the design cost over many more units.
[1] https://www.extremetech.com/computing/272096-3nm-process-nod...
However the popular "ultra" books and similar have to integrate more heavily to get close to 1". Even fans become hard to replace. Often there's no dimms (so you can't replace or add ram), even storage can be on the motherboard.
They are close to disposable unfortunately.
is it a consumer grade or corporate model?
Depending on your needs, we're been there for at least 10 years. I don't expect to have to upgrade (not counting increasing memory or storage as "ugrading") or replace any of the systems I own right now until they're over 10 years old. About half of my current machines are approaching their 10 year anniversary. Two are older than that. None of them need upgrading.
It seems unlikely that hardware improvements have stalled out right at the point where the performance level was just good enough.
Sure, it requires work to move off the legacy single-thread paradigm but it's also the economics of the software business these days. When you combine the increased control/lockdown by the mainstream platform owners and the decreasing willingness of customers to pay for software in the era of no-cost software, there isn't much incentive for new applications to appear. Notice that all of the major players are mostly focused on services and server side for the heavy lifting these days. It's not because the user hardware can't do it (hell, on the server side, CPUs and GPUs tend to be less powerful per-core), it's that it makes more economic sense for them to not bother doing interesting things on the users device.
It’s no accident that the biggest advances in the last ten years have been in areas not limited by single thread performance; machine learning & cloud computing.
For my needs? No. It's not that more performant systems (including software) aren't available, it's that they don't offer me any real benefit that justifies the hassle of replacing and reconfiguring my existing systems.
I think we're a year or two away before beefy, AAA games begin demanding 4 GHz minimum, and new CPUs barely offer over 4 GHz clocks as it is, so... we've already been there!
CPU power's been flatlined, especially when you compare it to GPU power.
Your overall point is accurate, but your metric is not.
But I'm far from an expert here (just an enthusiastic consumer), so what you're saying is news to me. The more flowers you can lay at what looks like the grave of Moore's law, the better. I don't like being a pessimist! :3
0: http://www.carestreamblog.com/blog/wp-content/uploads/2015/0...
1: https://images.anandtech.com/reviews/mac/MacPro2013/cpugpu.p...
CPU performance isn't increasing nearly as quickly as it used to, but it isn't stagnant. AMD's Zen 2 architecture looks incredibly promising. Credible leaks suggest a 16c/32t Ryzen 9 running at 4.3GHz base, with a 25% IPC improvement over the previous generation.
I think we're already there. Ivy Bridge machines from 2012 can still get the job done today if paired with a solid complement of RAM and an SSD.
Graphics is really the only area where those older machines are starting to be a limfac, as their inability to drive the latest 4K+ displays is starting to be more of an issue for normal people.
Otherwise, I was very happy with it in terms of performance.
I kept upgrading GPU and seeing only 20-30% CPU use while gaming. It was fine.
Some changes to how we build these things would be nice.
The heavily integrated motherboard model should die. A PCI slot on my old board made the whole system flake.
Discrete components I can stack are more my dream design. If my old CPU had been a sealed unit of some kind with a physical connector to the GPU, power, and such, and I could have simply swapped out the faulty port expansion “module”, that would be slick.
But one PCI port flaking means I have to bail on an otherwise perfectly functional CPU, RAM, PSU, and cooling kit.
Modern processors are faster than Sandy Bridge, but not by an overwhelming amount, and it's not clear how much of the improvement is due to smaller transistor sizes now that they no longer result in clock speed increases, rather than design improvements that are independent of transistor size.
There has to be a market for something with 80% of the performance and 2000% the reliability.
20 years ago we had Pentium 3 500MHz, 2 FLOPs/cycle, 1 GFlops.
GPU of my desktop PC computes 11 TFlops, that's 4+ orders of magnitude faster.
For CPUs alone it's indeed less impressive, $500 for that P3 inflated to $760, the comparable modern CPU is Threadripper 2920X, 16 flops/cycle * 12 cores * 3.5 GHz = 672 GFlops.
But they’re much lower quality (less flexible) FLOPs. If you don’t have much data parallelism or heavy conditional logic for each datum, you’re not going to come close to 11 teraflops.
Update: also, for other tasks, GPU flops are of higher quality than CPU.
VRAM bandwidth is ridiculous, much higher than system RAM, computations that are bound by RAM bandwidth will be much faster.
GPUs mask RAM latency with hardware multithreading, if your code is parallel and needs to access large amount of data randomly, a GPU will saturate these flops, a CPU will sleep most of the time waiting for data to arrive.
CPUs can be faster for RAM bandwidth-bound code, if there's not that much data and it fits in caches at least L3, CPU cache bandwidth is very good.
GPUs can be faster for heavily conditioned code. If it's the condition on RAM value, a CPU will stall or mispredict branches, both slow, a GPU will switch to another hardware thread which is very fast.
Despite these differences, I find flops quite useful in practice to compare performance of different computers across architectures of generations. Computers compute, flops measure how fast they do.
Using double precision SSE scaler ops, the Pentium III could execute one addition and one multiplication per cycle. (The throughput was the same with vector math because the Pentium III only had 64-bit SSE units. So a 128-bit packed multiply and a 128-bit packed add, four double precision operations, executed over two cycles.)
The only difference is that CPU computes stuff on 4-wide vectors of these numbers (modern CPUs up to 16), that GPU on 32-wide vectors (other modern GPUs up to 64).
But the comparison is against an old CPU, without big vector units.
Those non-vector calculations are very different, and far more flexible.
A brand new CPU core can do 3-4x as many separate operations per cycle, and is clocked 6-10x as fast.
Having more cores helps but it's still far behind.
Also for a fair price/performance ratio you probably want to compare to the 450MHz model at $230, so only a $350 CPU today ($280 equivalent by august). https://money.cnn.com/1999/08/23/technology/intel/
They’re both very flexible, just different.
For example, GPUs are better at vectorized condition code. CPUs were mostly fixed with AVX512 but these instructions are too new, only available on some servers.
Sure, there’re algorithms which don’t work on GPUs. A stream cipher would be very slow because requires single-thread performance, also GPUs don’t have AES hardware. A compiler is borderline impossible because inherently scalar and requires dynamic memory. Also GPUs don’t do double-precision math particularly fast.
Still, I think many users who need high-performance computing can utilize GPUs. They’re trickier to program, but this might be fixable with tools/languages, we have been programming classic computers for ~70 years, GPGPUs for just 12.
That's throughput, not flexibility. I would define flexibility in terms of how easily the instruction stream can vary per math operation. Full flexibility requires a lot more transistors per FLOP, which is why you can't use wildly different architectures to assess Moore's law, which is about transistor count.
And comparing transistors on Pentium 3 (including cache) to an RTX 2060 (including cache) it seems to be 34 million vs. 10800 million. That's two and a half orders of magnitude.
I used an i5-2500k for 7 years. Only retired it because the rest of the computer was falling apart and I had already had too many computers. I think that a CPU built in $CURRENT_YEAR will be completely usable in 10 years.
If you consider how far CPUs went in the ten years of Pentium 4 to Sandy Bridge vs Sandy Bridge to now... we certainly arrived to a plateu.
The types of games that would benefit greatly from such CPU are the ones that make heavy use of a Physics Engine.
>> single CPU with hundreds of cores would be really useful
Also putting more specialized parts like GPUs, FPGAs, ML accelerators either near or on the die. the A12 is a good example of this. Also, Intel's buyout of Xilinx shows that FPGAs can be put on die or near it. You could even put storage on the SOC.
Then there are the more esoteric methods like Quantum Computing.
Granted some processes are better at making some types of devices ... but maybe we'll see more one-size-fits all type chips in the future.
Now that I write this it seems like we are already most of the way there.
The article's interpretation is that Intel are struggling to get clock rates up on 10nm.
Fixed function hardware and more emphasis on efficient software.
We've had decades of software laziness, because anything less than an order of magnitude performance improvement on the software side was best shipped unoptimized, earlier, and just wait for hardware process shrinks.
We'll likely get back to something like the 60s, before the mass-marketing of the PC enabled funding of the relentless process improvements of the 80s, 90s, and 00s.
2. better cache hierarchy, larger/faster cache. (Cache misses and various cache access penalties reduced)
3. faster/low-latency ram access, probably new interconnects to minimize latency.
4. integrated sound/network chips and other peripherals. (essentially the path to System on the Chip)
5. more vector extensions and specialized instructions. (machine learning, graphics, high-precision floats,etc)
6. more secure processors and mitigations for future exploits. (e.g. this is 2020 processors, but our 2021 processor is immune to that)