And why is that because of "abusing their market position", as opposed to plainly and clearly being more difficult to get faster processors at 14 nm and lower resolutions (and with the low power requirements of today)?
Besides, the trend the article points to is a BS fad as I see it (and I've seen 5-6 of those play out in the last 30 years). ASICS and FPGAs wont even come close to bringing in as much cash as general purpose CPUs do for Intel.
As for AI, that needs to run upon hardware and whilst Intel is known for hardware they equaly do software and like most large companies. Often easier to let the little chaps do all the work, pick the best before it becomes big and buy them. Which can be cheaper than starting from scratch yourself and reduce the financial risks, whilst still getting in on the returns using smaller companies as research for a much more diverse skills and talents along with increased financial budget combined than any large company could dream of expending. So in some area's it is easier to have in house skills that can keep track of what everybody else is doing in that area and how it is progressing so that you are ready to swoop in and borg that lego block for your collection and reduce your financial risk greatly. More so in area's that are still very much maturing.
Okay, let's say that Intel hasn't been under investing in improving the performance of their cpus. They still have 63% margins across their entire product line, they still don't have 8 core cpu options below $1000. They still don't have consumer cpu options that ditch the integrated graphics.
All of which is only possible because they have had a monopoly.
You got it backwards: them having a monopoly is only possible because it hasn't been possible for anyone else to produce more efficient desktop/laptop CPUs. On the mobile, where there are competent competitors, it's a different story.
It's not as if if they sacrificed their margins they'd magically be able to produce faster processors. Just cheaper ones.
And why would they have "consumer cpu options that ditch the integrated graphics"? What that would accomplish, when there's a ton of cpu being wasted otherwise?
Intel has patents over the x86 instruction set.
>> It's not as if if they sacrificed their margins they'd magically be able to produce faster processors. Just cheaper ones.
They have produced slower processor on purpose. Instead of soldering the chip to the heat spreader, they started using a very inefficient thermal paste. Replacing it leads to 20K better temps. It hurts both the speed and the expected life time of the processor. But what's wrong if the customer needs a new CPU in 3 years ...
>> And why would they have "consumer cpu options that ditch the integrated graphics"? What that would accomplish, when there's a ton of cpu being wasted otherwise?
The graphics units occupy more than 50% of the die on recent Intel mainstream CPUs. Instead of GPU they could put 8 cores in chips like 7700K and save money.
Not the x86 instruction sets - that's out of patent protection a while ago (patents are max 20 years).
Maybe amd64 (which is more AMDs parents).
PS: GPU's might seem like something you want on the CPU, but bandwidth is a killer worse they add heat which directly lowers CPU performance.
I've been waiting for a few generations now to get an Intel CPU with decent iGPU but it's really been stagnant. I gave up the wait and pre-ordered a Ryzen last week.
I do still think it makes sense for Intel to ship CPUs with those integrated CPUs, although maybe it wouldn't be a bad idea to offer a chip with more cores and no GPU for the enthusiasts.
Why? iGPU's increase production costs AND reduce both CPU and GPU performance. If Intel wants to produce a separate GPU then more power to them, but that's not what they are doing they are a monopoly extending their dominance to another market at the cost of consumers aka us.
Remember, resistance increases with heat, so even it there where zero manufacturing or performance costs it still increases power consumption.
Furthermore, these iGPUs are incredibly energy efficient, and obviously small. That's great for battery life and PC size.
And that same future applies not only for neural computing, but for a field called approximate-computing, i.e. computing where results aren't accurate.Some/many signal and image processing work well with that. I've also seen some research about doing scientific computing on approximate hardware and correcting errors.
I worked for a while as the software/FPGA guy on a high-power RF amplifier for MRI machines. I worked with some very talented RF engineers. There were obviously some parts of the chain that were analog (like the preamps and high power amp itself). But lots of it was being replaced with digital (DSP techniques) in the next generation and the RF engineers loved it. Filters no longer needed a 15 minute tuning session by a technician with a network analyzer. A Hilbert transform is far easier and better in digital than analog. Design changes could be experimented with in a day instead of PCB spins that took weeks.
Given all these constraints I am actually quite excited to hear more of the dissenting view. Can you describe some of the research you mentioned in the last sentence? I have not heard of it and if true it would be at least interesting engineering, even if right now I doubt it would actually work.
Most machine learning algorithms are by design resilient to random noise. They can even be learned to be resilient to systematic noise (due to say.. variable hardware performance in mass production).
In those cases; a low power analog device with lower reliability guarantees is 'ok'.
I suspect the "decision engines" (the computation enforcing complex logic based on sensor readings) will probably continue to be on CPU's for some time.
It would surprise me if even unfiltered analog noise in the sensors significantly changed recall. I suppose I don't know off-hand know the magnitude of noise in typical analog systems.
As a proof of concept they did a GPS chip , with 66 times higher energy efficiency.
There are others working on analog neural compute , some released good research results .
Also, there's a huge application domain in image preprocessing: the pixel value is analog and noisy anyway, so building an analog primary visual cortex with a bank of common filters (line segments, stable features etc.) makes a lot of sense.
ADD: I'm also thinking about accelerating RANSAC-type algorithms, where you perform lots of preliminary evaluations to filter out the outliers. I bet one can cheaply implement an "100-argmins of 100 x 100 fixed10 distance matrix" computation in a handful of cycles with analog circuits. And high-dimensional RANSAC-type algorithms are what needed for robust and precise 3D visual navigation.
But they might not need that powerful CPU anymore, if computational heavy task happens elsewhere anyway. So as time goes, CPU will become less critical, meaning less money for Intel
Therefore you'll need a capable several-core CPU anyway, and unless the memory is shared (which means you have a SoC, where there's no clear leader AFAIU), moving data in and out becomes a major bottleneck.
I think for most systems where explainability and audit trail are important it will be easier (I mean cheaper) to build on a powerful CPU from the start, and when the system is successful (i.e. approved by real customers), then the highly-parallel parts may be factored out to special hardware. Using them from the start is very risky.
These thoughts are from experience building an all-weather computer vision system for traffic control applications (99.5+% vehicle classification, with errors requiring human intervention and explanation), and being rather familiar with a company which makes specialized ID-recognizing mobile apps.
If that's all it is isn't that going to be pretty much a simple knob for Intel to turn to respond? They already have Xeon which doesn't use the space on graphics.
The main reason for pushing iGPU on desktop so hard was so the same chips could be pretty much used on mobile I guess?
If only, intel has been abusing their market position and pushing out "upgrades" that barely have a performance improvement over the previous generation.
AI is not going to eat intel's lunch, all those computers still require cpus. AMD on the other hand may eat intel's lunch by releasing powerful multicore processors for half the price all because they don't waste space on the die for things like integrated graphics.