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From what I gather online, intel is entering into an exestential crisis as homegrown chips and GPUs are replacing CPUs in the battle for tomorrow's data center.

From what I gather online, intel is entering into an exestential crisis as homegrown chips and GPUs are replacing CPUs in the battle for tomorrow's data center. All of the interesting research from Google and others all have one thing in common: a custom asic processor.

puzzle 10y ago
You forgot that Intel bought Nervana Systems last year, right before IDF. They develop custom ASIC processors for machine learning.
- mtgx 10y ago
  Which doesn't necessarily mean anything for Intel's future in machine learning. Intel bought an ARM chip maker before, too, and because of internal conflict of interest with the x86 group, it ended-up killing the ARM division. Will the same happen to other non-x86 groups? Who knows. But we also see that even Intel's previous "accelerator" divisions have moved focus to being "CPU-driven" as well (Xeon Phi), so we have that data point. I also remember some of the articles from a decade ago that were sure Intel's "might" would help it succeed in the mobile market. I'm not entirely sure if it's as bad for Intel in the machine learning market, because right now there's pretty much only one competitor rather than many. It's usually much easier to be second in a market than the 5th, as some customers would inevitably get some raw deals from the incumbent, and they'll want an alternative. But if the machine learning market starts becoming entrenched around GPUs, that second player may end-up being AMD, which I think will have decent alternatives to Nvidia's training and inference GPUs in a year or two (mainly for 2019 with 7nm Navi, though).
  - jacquesm 10y ago
    Xeon Phi was a nice idea without much in terms of support or distribution. I tried getting one through various sources, they were advertised just about everywhere and even at huge discounts but actually getting one has so far proved to be impossible. And it still remains to be seen how useful it would have been, whereas a $600 GPU can be dropped into your computer and put to work in about half an hour. The hardest part is the driver installation and NVIDIA's dumb 'sign up before you download' strategy.
    - slizard 10y ago
      > ...but actually getting one has so far proved to be impossible. Where/when did you try buying? I didn't think it was difficult to get the 1st-gen Phi cards while they were selling them -- especially as it seemed like nobody wanted them. Admittedly I never bought any myself, my employer picked up some 31S1P from a local server OEM for testing when they were massively discounted. Note that the Knights Landing, the current generation is released socketed only for now, so you'd need to buy a server or the ninja dev platforms Intel sells through Colfax.
      - jacquesm 10y ago
        I tried two online vendors that were actively soliciting customers but when push came to shove they couldn't deliver. Right around the same time I ordered an FPGA dev kit and an NVIDIA board, solved the problem I was dealing with and moved on. Still, I would have very much liked to play around with the 'phi'. > Admittedly I never bought any myself, my employer picked up some 31S1P from a local server OEM for testing when they were massively discounted. That's the kind of deal I was looking at too. Maybe they were massively discounted because they were already EOL'd?
        slizard 10y ago
        > Still, I would have very much liked to play around with the 'phi'. Well, if you're still interested, KNL is a significantly better architecture, so you could just try now. > That's the kind of deal I was looking at too. Maybe they were massively discounted because they were already EOL'd? Refreshed my memory and I was wrong; there was some discussion to jump on the $125-$195/card deal Colfax had late 2014 [1] (likely dumping stock due to EOL), but we figured that they are not even worth that much. In comparison, the one card we have was actually bought just 10-11 months before for ~$1500. [1] http://www.colfax-intl.com/nd/xeonphi/31s1p-promo.aspx http://www.colfax-intl.com/nd/xeonphi/31s1p-promo.aspx
  - trhway 10y ago
    > Intel bought an ARM chip maker before, too, and because of internal conflict of interest with the x86 group, it ended-up killing the ARM division. reminds Sun in 1998 and the onward years when SPARC was like North Korea border patrol in preventing any x86 penetration into Sun even though it was clear that x86 was the future.
    - gonzo 10y ago
      Sorry? The Sun 386i "Roadrunner" was launched in 1988. A release of Solaris for PC compatibles began shipping in 1993. In 2002, Sun introduced its first general purpose x86 system, the LX50. In 2003, Sun announced a strategic alliance with AMD to produce x86/x64 servers based on AMD's Opteron processor; this was followed shortly by Sun's acquisition of Kealia, a startup founded by original Sun founder Andy Bechtolsheim, which had been focusing on high-performance AMD-based servers. Hardware began shipping in 2004.
      - trhway 10y ago
        it would be very helpful to understand whether you know what you're talking about if you also stated whether you've been there or not :) I spent there about a decade.
  - orionblastar 10y ago
    If I ran Intel, I'd keep both x86 and ARM chips and make a motherboard using x86 and ARM chips to work together with shared memory. The ARM could be given tasks to do that free up the x86 chip to make the system run faster. Plus ARM operating systems could be run in a virtual machine using the ARM chip to develop or test apps out.
    - wolfgke 10y ago
      > The ARM could be given tasks to do that free up the x86 chip to make the system run faster. What advantage does using an ARM core for this have over simply using an x86 core?
      - orionblastar 10y ago
        The ARM chip would use less power than the x86 chip. It would be greener computing.
        wolfgke 10y ago
        Intel also produces x86 cores that use very low power (e.g. Intel Quark). It probably would be much easier (and much more in line with Intel's corporate objectives) to add such a low-power x86 core than an ARM core if there is such a desire. So I stand by my point: > What advantage does using an ARM core for this have over simply using an x86 core?
        orionblastar 10y ago
        When I went into UM Rolla in 1986 I wanted to study computer engineering. I had liked the Amiga and its co-processors. It proved to me that if you had processors that took time off the main CPU the OS and apps can run faster. But if you used 68000, Z80, 6502 chips as a co-processor to an 8088 CPU or Intel X86 you could assign tasks to them to free up the main CPU and even doing parallel processing. Sure Intel can make low power CPUs but if you have an X86 and ARM chip working together, like an Apple Macintosh that wants to go to ARM eventually but needs X86 code to run as well, if they make Macs with an Intel X86 and ARM chips they can run iOS apps on the ARM and MacOS apps on the Intel to run more apps than a Mac normally has. Edit: I like to also add if Apple put am ARM chip on their Macs with an Intel chip and required the ARM for newer MacOS versions they can lock out the Hackintosh users from pirating their OS.
  - gonzo 10y ago
    > Intel bought an ARM chip maker before, too, and because of internal conflict of interest with the x86 group, it ended-up killing the ARM division. Intel acquired Digital's StrongARM unit as part of a settlement for a patent infringement suit. https://www.cnet.com/news/intel-digital-settle-suit/ https://www.cnet.com/news/intel-digital-settle-suit/
    - chronic940 10y ago
      And since then, has Digital Strong ARM produced anything? You are further proving his point.
  - pcwalton 10y ago
    > I also remember some of the articles from a decade ago that were sure Intel's "might" would help it succeed in the mobile market. You don't even have to look that far back. As recently as two years ago, I remember comments here proclaiming with certainty that Intel would crush ARM because they had finally broken through the power wall. I think a lot of folks, Intel included, have a strong belief in the inevitability of x86 dominance. But ultimately most of the inherent value of x86 is compatibility with Windows software (and DOS before it). Take that away and a lot of the advantages disappear. (Not to downplay the impressive technical and process achievements Intel has made, of course...)
- RealityVoid 10y ago
  They also bought Movidius.
CalChris 10y ago
custom asic processor Is that like La Brea Tarpit? Just say ASIC. An ASIC is an Application Specific Integrated Circuit. In particular, a GPU is not an ASIC. Saying Custom Application Specific Integrated Circuit Processor is doubly redundant. https://en.wikipedia.org/wiki/Application-specific_integrated_circuit https://en.wikipedia.org/wiki/Application-specific_integrate... https://en.wikipedia.org/wiki/Graphics_processing_unit https://en.wikipedia.org/wiki/Graphics_processing_unit
- gumby 10y ago
  I don't think so: a GPU is application-specific, (not GPGPU if such a thing really exists), as are other ones. Lisp Machine chips were application-specific. Various application-specific accelerator chips have been designed for FP, video decoding, security etc. Where you're right is that Nirvana was a chip company founded for this ASIC and I think the term "custom" is misnomer. You could argue that Google isn't a chip company so theirs are custom.
  - deleted 10y ago
    [deleted]
- jacquesm 10y ago
  A GPU actually also is an ASIC, it just happens to be that that application is a different one.
  - wyldfire 10y ago
    I wouldn't describe something capable of executing something quite so flexible as shaders/kernels an ASIC. It sounds much closer to a CPU -- designed to execute nearly arbitrary software. And if we're including chips that execute software as ASICs, then it seems to really water down the term to be meaningless.
eklitzke 10y ago
The ASIC processors Google use are for inference, not training. Having efficient inference is great, but training is what uses up most of the compute power. The battle between GPUs and CPUs in the datacenter is real, but I wouldn't count out regular CPUs yet. Even with GPU-ready frameworks like Tensorflow, Theano, and Pytorch, it still requires a fair amount of domain expertise to get good performance out of GPUs. These frameworks can automatically offload compute kernels to the GPU, but actually getting good performance still requires you to understand the memory model, what data to pin in GPU memory, how to use the limited GPU memory effectively, etc. The new instruction sets that Intel have been adding (e.g. all of the recent vectorized FMA instructions) go a long way to making Intel CPUs competitive for a lot of this ASIC/GPU stuff out of the box for things like neural networks. Nvidia has been off to a really great start in this area, and they have carved out a very respectable lead, but I think it's premature to say that Intel (or AMD) are having an existential crisis.
- puzzle 10y ago
  Correction: the ASIC processors that Google used in 2015 and just talked about, two years later, were for inference. We don't know what they use now and how. The paper hints that training might happen in a later revision. You have to wonder why they started with inference and not the other way round. Perhaps it's the case that you train a model once, then use it for predictions many, many, many times, thus using more aggregate compute power. (I used to work at Google, but the above is based on public information and speculation alone.) The paper does confirm that CPUs are closer to GPUs for smaller or latency-sensitive workloads than most people assumed. At least on 2015-vintage hardware, that is. And they're a lot less trouble to set up and provision.
  - jtmcmc 10y ago
    Training can happen asynchronously and in batch - inference needs to be low latency and have massive parallelism because it's powering millions of android devices voice to text recognition - for instance.
  - _delirium 10y ago
    > You have to wonder why they started with inference and not the other way round. At least from what I know of ASICs (which is admittedly pretty high-level), this direction is the natural way I'd expect it to go, because the inference problem is much closer in scale to the kind of problem ASICs are already widely used for and good at. Fast inference can be done with a chip of a size similar to what you see in existing ASICs, like specialized DSP chips. Speeding up training is a lot more speculative. You could try scaling up to massively powerful individual chips that can each replace an NVidia GPU (but with lower cost / lower power budget), or improve distributed training so you can effectively use a much larger number of much weaker customized chips. And there are more significant memory and memory-bandwidth issues with training, so it's less of the pure-compute type of problem that ASICs are more often clear wins for. All of which is probably possible with enough effort, but less straightforward, especially to end up with something that beats GPUs on relevant metrics.
lima 10y ago
Most of the workload is still on CPUs - so the main competitor would be ARM server CPUs?
MichaelMoser123 10y ago
They put up a lot of money for the recent acquisition of mobileye, is a company in problems supposed to do big acquisitions ?
- nextweek2 10y ago
  Yes. If you are a large multi-national where your product pipeline is shallow due to competitors creation better products than your R&D. I think the Xeon Phi is an example of something which fell flat.
visionscaper 10y ago
The Von Neumann architecture is dead, IMHO future computing architectures involve many cores, all with local memory (not just a bit of cache), Tensor computing capabilities and very high bandwidth connecting these cores. Intel has Xeon Phi, but what else are they doing? There is so much opportunity in new computing architectures but it seems like Intel has been taken by surprise. I hope this giant wakes up.
- LgWoodenBadger 10y ago
  Well, the core with its own memory is still a Von Neumann machine, is it not?
  - adrianratnapala 10y ago
    More likely a Harvard machine, i.e. one where the instruction memory is distinct from the data memory. It is essentially what modern cores have with a their split caches, except those things are called "caches" because the architecture then has a lot of machinery for pretending to be a von Neumann Machine with a huge memory. This pretending is done because that's the kind of machine that software expects. But the first unices ran on machines with memories only as big as a modern L1-cache. Moreover, networking and parallelism are everyday issues for software now. It will be interesting to see if software evolves to explicitly recognise that it is really running on a network of small machines + a hierarchy of big external storage devices, of which DRAM is just one.
  - visionscaper 10y ago
    Yes, true, Von Neumann is dead, long live Von Neuman! ;) The difference is of course that we should not see the whole cluster of cores as a Von Neumann machine, the memory bandwidth is just too low to drive multiple cores with some external memory, memory must be as close as possible to the cores themselves. Quote from wtallis lower in this sub-thread : > You can't pretend that memory is all equidistant from the processor cores unless you want everything running equally slow.
- ww520 10y ago
  NUMA style architecture has been existed for a long while now. It doesn't seem to take over the world.
Gibbon1 10y ago
We're at a crossroads. Fifteen or so years ago I read someone mentioning that the ratio of processor state transitions per bit of program state was growing almost exponentially. Anecdote: Starting in the early eighties I started using CAD programs. And they were painfully slow. Yet around 1995 suddenly they weren't 'slow'. That was huge. Since then improvements in processor speed and memory hasn't provided nearly as much functional improvement. What I think is we're in a world were current processor designs are over kill of much of the programming tasks people need done. What people want is not more speed but better efficiency. That points towards using excess silicon to implement task specific coprocessors, not for more unneeded GPU performance gains. This leaves Intel in a bad spot. Especially since everyone and their dog is loath to design in Intel parts if they can help it.
- m_mueller 10y ago
  It was a bit of an eye opener when I saw an old NT 4.0 server again. I think it had around 500 Mhz and 128MB, i.e. quite a beast for its time. All clicks had immediate on-screen results, a real pleasure to navigate, felt like a 90FPS game. Same was true even for something like iPhone 3GS and iOS3. Nowadays processors are so fast that software doesn't need to be optimized much anymore, and this is what we got. Upside is we can soon have true cross platform GUI apps that are smooth everywhere - until the next big platform like AR comes.
lightedman 10y ago
I think it goes a bit deeper than that. Intel is losing the power it had as a general-purpose CPU maker. Now that technology and brains are improving, others are figuring out more efficient ways of handling specific tasks that the x86/x64 CPU used to do in an efficient manner. And intel doesn't have these people. They don't want the embarrassment and are too entrenched in their own processes. The technology as of this year can compete or stomp intel into the ground. We can program ASICs to handle any task, just about as fast as it can handle the electron flow. The only real setback is the fact the instructions are not hard-coded in the actual physical logic. That's literally the only slow-down. Were it not for that translation layer right there, ASICs would come out on top, no problem. Specialization is the future. General purpose was good enough for 30 years, it's time to move on.

puzzle 10y ago

You forgot that Intel bought Nervana Systems last year, right before IDF. They develop custom ASIC processors for machine learning.

mtgx 10y ago
Which doesn't necessarily mean anything for Intel's future in machine learning. Intel bought an ARM chip maker before, too, and because of internal conflict of interest with the x86 group, it ended-up killing the ARM division. Will the same happen to other non-x86 groups? Who knows. But we also see that even Intel's previous "accelerator" divisions have moved focus to being "CPU-driven" as well (Xeon Phi), so we have that data point. I also remember some of the articles from a decade ago that were sure Intel's "might" would help it succeed in the mobile market. I'm not entirely sure if it's as bad for Intel in the machine learning market, because right now there's pretty much only one competitor rather than many. It's usually much easier to be second in a market than the 5th, as some customers would inevitably get some raw deals from the incumbent, and they'll want an alternative. But if the machine learning market starts becoming entrenched around GPUs, that second player may end-up being AMD, which I think will have decent alternatives to Nvidia's training and inference GPUs in a year or two (mainly for 2019 with 7nm Navi, though).
- jacquesm 10y ago
  Xeon Phi was a nice idea without much in terms of support or distribution. I tried getting one through various sources, they were advertised just about everywhere and even at huge discounts but actually getting one has so far proved to be impossible. And it still remains to be seen how useful it would have been, whereas a $600 GPU can be dropped into your computer and put to work in about half an hour. The hardest part is the driver installation and NVIDIA's dumb 'sign up before you download' strategy.
  - slizard 10y ago
    > ...but actually getting one has so far proved to be impossible. Where/when did you try buying? I didn't think it was difficult to get the 1st-gen Phi cards while they were selling them -- especially as it seemed like nobody wanted them. Admittedly I never bought any myself, my employer picked up some 31S1P from a local server OEM for testing when they were massively discounted. Note that the Knights Landing, the current generation is released socketed only for now, so you'd need to buy a server or the ninja dev platforms Intel sells through Colfax.
    - jacquesm 10y ago
      I tried two online vendors that were actively soliciting customers but when push came to shove they couldn't deliver. Right around the same time I ordered an FPGA dev kit and an NVIDIA board, solved the problem I was dealing with and moved on. Still, I would have very much liked to play around with the 'phi'. > Admittedly I never bought any myself, my employer picked up some 31S1P from a local server OEM for testing when they were massively discounted. That's the kind of deal I was looking at too. Maybe they were massively discounted because they were already EOL'd?
      - slizard 10y ago
        > Still, I would have very much liked to play around with the 'phi'. Well, if you're still interested, KNL is a significantly better architecture, so you could just try now. > That's the kind of deal I was looking at too. Maybe they were massively discounted because they were already EOL'd? Refreshed my memory and I was wrong; there was some discussion to jump on the $125-$195/card deal Colfax had late 2014 [1] (likely dumping stock due to EOL), but we figured that they are not even worth that much. In comparison, the one card we have was actually bought just 10-11 months before for ~$1500. [1] http://www.colfax-intl.com/nd/xeonphi/31s1p-promo.aspx http://www.colfax-intl.com/nd/xeonphi/31s1p-promo.aspx
- trhway 10y ago
  > Intel bought an ARM chip maker before, too, and because of internal conflict of interest with the x86 group, it ended-up killing the ARM division. reminds Sun in 1998 and the onward years when SPARC was like North Korea border patrol in preventing any x86 penetration into Sun even though it was clear that x86 was the future.
  - gonzo 10y ago
    Sorry? The Sun 386i "Roadrunner" was launched in 1988. A release of Solaris for PC compatibles began shipping in 1993. In 2002, Sun introduced its first general purpose x86 system, the LX50. In 2003, Sun announced a strategic alliance with AMD to produce x86/x64 servers based on AMD's Opteron processor; this was followed shortly by Sun's acquisition of Kealia, a startup founded by original Sun founder Andy Bechtolsheim, which had been focusing on high-performance AMD-based servers. Hardware began shipping in 2004.
    - trhway 10y ago
      it would be very helpful to understand whether you know what you're talking about if you also stated whether you've been there or not :) I spent there about a decade.
- orionblastar 10y ago
  If I ran Intel, I'd keep both x86 and ARM chips and make a motherboard using x86 and ARM chips to work together with shared memory. The ARM could be given tasks to do that free up the x86 chip to make the system run faster. Plus ARM operating systems could be run in a virtual machine using the ARM chip to develop or test apps out.
  - wolfgke 10y ago
    > The ARM could be given tasks to do that free up the x86 chip to make the system run faster. What advantage does using an ARM core for this have over simply using an x86 core?
    - orionblastar 10y ago
      The ARM chip would use less power than the x86 chip. It would be greener computing.
      - wolfgke 10y ago
        Intel also produces x86 cores that use very low power (e.g. Intel Quark). It probably would be much easier (and much more in line with Intel's corporate objectives) to add such a low-power x86 core than an ARM core if there is such a desire. So I stand by my point: > What advantage does using an ARM core for this have over simply using an x86 core?
        orionblastar 10y ago
        When I went into UM Rolla in 1986 I wanted to study computer engineering. I had liked the Amiga and its co-processors. It proved to me that if you had processors that took time off the main CPU the OS and apps can run faster. But if you used 68000, Z80, 6502 chips as a co-processor to an 8088 CPU or Intel X86 you could assign tasks to them to free up the main CPU and even doing parallel processing. Sure Intel can make low power CPUs but if you have an X86 and ARM chip working together, like an Apple Macintosh that wants to go to ARM eventually but needs X86 code to run as well, if they make Macs with an Intel X86 and ARM chips they can run iOS apps on the ARM and MacOS apps on the Intel to run more apps than a Mac normally has. Edit: I like to also add if Apple put am ARM chip on their Macs with an Intel chip and required the ARM for newer MacOS versions they can lock out the Hackintosh users from pirating their OS.
- gonzo 10y ago
  > Intel bought an ARM chip maker before, too, and because of internal conflict of interest with the x86 group, it ended-up killing the ARM division. Intel acquired Digital's StrongARM unit as part of a settlement for a patent infringement suit. https://www.cnet.com/news/intel-digital-settle-suit/ https://www.cnet.com/news/intel-digital-settle-suit/
  - chronic940 10y ago
    And since then, has Digital Strong ARM produced anything? You are further proving his point.
- pcwalton 10y ago
  > I also remember some of the articles from a decade ago that were sure Intel's "might" would help it succeed in the mobile market. You don't even have to look that far back. As recently as two years ago, I remember comments here proclaiming with certainty that Intel would crush ARM because they had finally broken through the power wall. I think a lot of folks, Intel included, have a strong belief in the inevitability of x86 dominance. But ultimately most of the inherent value of x86 is compatibility with Windows software (and DOS before it). Take that away and a lot of the advantages disappear. (Not to downplay the impressive technical and process achievements Intel has made, of course...)
RealityVoid 10y ago
They also bought Movidius.

CalChris 10y ago

custom asic processor Is that like La Brea Tarpit? Just say ASIC. An ASIC is an Application Specific Integrated Circuit. In particular, a GPU is not an ASIC. Saying Custom Application Specific Integrated Circuit Processor is doubly redundant. https://en.wikipedia.org/wiki/Application-specific_integrated_circuit https://en.wikipedia.org/wiki/Application-specific_integrate... https://en.wikipedia.org/wiki/Graphics_processing_unit https://en.wikipedia.org/wiki/Graphics_processing_unit

gumby 10y ago
I don't think so: a GPU is application-specific, (not GPGPU if such a thing really exists), as are other ones. Lisp Machine chips were application-specific. Various application-specific accelerator chips have been designed for FP, video decoding, security etc. Where you're right is that Nirvana was a chip company founded for this ASIC and I think the term "custom" is misnomer. You could argue that Google isn't a chip company so theirs are custom.
- deleted 10y ago
  [deleted]
jacquesm 10y ago
A GPU actually also is an ASIC, it just happens to be that that application is a different one.
- wyldfire 10y ago
  I wouldn't describe something capable of executing something quite so flexible as shaders/kernels an ASIC. It sounds much closer to a CPU -- designed to execute nearly arbitrary software. And if we're including chips that execute software as ASICs, then it seems to really water down the term to be meaningless.

eklitzke 10y ago

The ASIC processors Google use are for inference, not training. Having efficient inference is great, but training is what uses up most of the compute power. The battle between GPUs and CPUs in the datacenter is real, but I wouldn't count out regular CPUs yet. Even with GPU-ready frameworks like Tensorflow, Theano, and Pytorch, it still requires a fair amount of domain expertise to get good performance out of GPUs. These frameworks can automatically offload compute kernels to the GPU, but actually getting good performance still requires you to understand the memory model, what data to pin in GPU memory, how to use the limited GPU memory effectively, etc. The new instruction sets that Intel have been adding (e.g. all of the recent vectorized FMA instructions) go a long way to making Intel CPUs competitive for a lot of this ASIC/GPU stuff out of the box for things like neural networks. Nvidia has been off to a really great start in this area, and they have carved out a very respectable lead, but I think it's premature to say that Intel (or AMD) are having an existential crisis.

puzzle 10y ago
Correction: the ASIC processors that Google used in 2015 and just talked about, two years later, were for inference. We don't know what they use now and how. The paper hints that training might happen in a later revision. You have to wonder why they started with inference and not the other way round. Perhaps it's the case that you train a model once, then use it for predictions many, many, many times, thus using more aggregate compute power. (I used to work at Google, but the above is based on public information and speculation alone.) The paper does confirm that CPUs are closer to GPUs for smaller or latency-sensitive workloads than most people assumed. At least on 2015-vintage hardware, that is. And they're a lot less trouble to set up and provision.
- jtmcmc 10y ago
  Training can happen asynchronously and in batch - inference needs to be low latency and have massive parallelism because it's powering millions of android devices voice to text recognition - for instance.
- _delirium 10y ago
  > You have to wonder why they started with inference and not the other way round. At least from what I know of ASICs (which is admittedly pretty high-level), this direction is the natural way I'd expect it to go, because the inference problem is much closer in scale to the kind of problem ASICs are already widely used for and good at. Fast inference can be done with a chip of a size similar to what you see in existing ASICs, like specialized DSP chips. Speeding up training is a lot more speculative. You could try scaling up to massively powerful individual chips that can each replace an NVidia GPU (but with lower cost / lower power budget), or improve distributed training so you can effectively use a much larger number of much weaker customized chips. And there are more significant memory and memory-bandwidth issues with training, so it's less of the pure-compute type of problem that ASICs are more often clear wins for. All of which is probably possible with enough effort, but less straightforward, especially to end up with something that beats GPUs on relevant metrics.

lima 10y ago

Most of the workload is still on CPUs - so the main competitor would be ARM server CPUs?

MichaelMoser123 10y ago

They put up a lot of money for the recent acquisition of mobileye, is a company in problems supposed to do big acquisitions ?

nextweek2 10y ago
Yes. If you are a large multi-national where your product pipeline is shallow due to competitors creation better products than your R&D. I think the Xeon Phi is an example of something which fell flat.

visionscaper 10y ago

The Von Neumann architecture is dead, IMHO future computing architectures involve many cores, all with local memory (not just a bit of cache), Tensor computing capabilities and very high bandwidth connecting these cores. Intel has Xeon Phi, but what else are they doing? There is so much opportunity in new computing architectures but it seems like Intel has been taken by surprise. I hope this giant wakes up.

LgWoodenBadger 10y ago
Well, the core with its own memory is still a Von Neumann machine, is it not?
- adrianratnapala 10y ago
  More likely a Harvard machine, i.e. one where the instruction memory is distinct from the data memory. It is essentially what modern cores have with a their split caches, except those things are called "caches" because the architecture then has a lot of machinery for pretending to be a von Neumann Machine with a huge memory. This pretending is done because that's the kind of machine that software expects. But the first unices ran on machines with memories only as big as a modern L1-cache. Moreover, networking and parallelism are everyday issues for software now. It will be interesting to see if software evolves to explicitly recognise that it is really running on a network of small machines + a hierarchy of big external storage devices, of which DRAM is just one.
- visionscaper 10y ago
  Yes, true, Von Neumann is dead, long live Von Neuman! ;) The difference is of course that we should not see the whole cluster of cores as a Von Neumann machine, the memory bandwidth is just too low to drive multiple cores with some external memory, memory must be as close as possible to the cores themselves. Quote from wtallis lower in this sub-thread : > You can't pretend that memory is all equidistant from the processor cores unless you want everything running equally slow.
ww520 10y ago
NUMA style architecture has been existed for a long while now. It doesn't seem to take over the world.

Gibbon1 10y ago

We're at a crossroads. Fifteen or so years ago I read someone mentioning that the ratio of processor state transitions per bit of program state was growing almost exponentially. Anecdote: Starting in the early eighties I started using CAD programs. And they were painfully slow. Yet around 1995 suddenly they weren't 'slow'. That was huge. Since then improvements in processor speed and memory hasn't provided nearly as much functional improvement. What I think is we're in a world were current processor designs are over kill of much of the programming tasks people need done. What people want is not more speed but better efficiency. That points towards using excess silicon to implement task specific coprocessors, not for more unneeded GPU performance gains. This leaves Intel in a bad spot. Especially since everyone and their dog is loath to design in Intel parts if they can help it.

m_mueller 10y ago
It was a bit of an eye opener when I saw an old NT 4.0 server again. I think it had around 500 Mhz and 128MB, i.e. quite a beast for its time. All clicks had immediate on-screen results, a real pleasure to navigate, felt like a 90FPS game. Same was true even for something like iPhone 3GS and iOS3. Nowadays processors are so fast that software doesn't need to be optimized much anymore, and this is what we got. Upside is we can soon have true cross platform GUI apps that are smooth everywhere - until the next big platform like AR comes.

lightedman 10y ago

I think it goes a bit deeper than that. Intel is losing the power it had as a general-purpose CPU maker. Now that technology and brains are improving, others are figuring out more efficient ways of handling specific tasks that the x86/x64 CPU used to do in an efficient manner. And intel doesn't have these people. They don't want the embarrassment and are too entrenched in their own processes. The technology as of this year can compete or stomp intel into the ground. We can program ASICs to handle any task, just about as fast as it can handle the electron flow. The only real setback is the fact the instructions are not hard-coded in the actual physical logic. That's literally the only slow-down. Were it not for that translation layer right there, ASICs would come out on top, no problem. Specialization is the future. General purpose was good enough for 30 years, it's time to move on.