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The rise of AI is creating new variety in the chip market, and trouble for Intel
- Filligree 10y agoThe article is visible at first, but once the page loads entirely it disappears.
- SideburnsOfDoom 10y agoIt happens to me too. I think it's The Economist's paywall in action. It's pretty annoying that this article is linkable but not-really on the web.
- Filligree 10y agoThat's a broken paywall, then. I'm on mobile, so not using any ad blocker, but there was no hint of explanation.
- Baeocystin 10y agoI had the same thing happen, but it stayed open fine once I tried the link in incognito mode. Probably just a mis-coded "you've reached your article limit for the month" deal.
- fstephany 10y agoSame in Firefox for me. I switched to "Reader View" (I love this feature) and the text appeared.
- ghaff 10y agoAI/ML/etc. may be part of it. But the other factor is that you can't just wait 18 months any longer for a new generation of x86 to be a lot [EDIT] faster. That was the big problem with specialized architectures historically. The volume architecture would catch up soon enough without you having to rewrite software to optimize for some different design of processor. That's no longer the case so specialized designs for the compute-hungry workload du jour (which happens to be AI at the moment) are starting to look a lot more attractive.
- friedman23 10y ago> Instead of making ASICS or FPGAs, Intel focused in recent years on making its CPU processors ever more powerful 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.
- petra 10y agoASIC/FPGA or even GPU's aren't the future of neural computing. The future is analog(orders of magnitude perf/watt and perf/$). That require older fabs, optimized for analog, and having good embedded flash, which TSMC has and Intel mostly hasn't got. 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.
- krastanov 10y agoBut analog computers do not permit error correction! Crosstalk of 10% does not affect digital data busses, but ruins analog. ECC memory is trivial with digital but not possible even in theory with analog. This (and programmability) is why we moved from super fast analog to slow digital half a century ago. 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.
- cjhanks 10y agoA significant portion of any real-world AI system is dedicated to extracting relevant features from raw sensor information. Doing that digitally has proven to have a very high cost rated in both watts and operating temperature. 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.
- doener 10y agohttps://news.ycombinator.com/item?id=13732878 https://news.ycombinator.com/item?id=13732878
- Zenst 10y agoIt is more a case most cpu intensive demands being addressed by dedicated chips, as we always have had. Many area's of information technology move from general cpu's towards dedicated silicon. Even CPU's adapt and add instructions and with that small area's of silicon space for some dedicated demands (think MMX, AVX, AES,...). This is no change at all in what we already have. It is when we finally dedicate all tasks down to dedicated silicon that the glue of a CPU processing wise will diminish. But then CPU's of today are constantly adapting and I'd say the C in CPU is better defined as Centralised rather than Central. For me, I'm looking forward to a AI grammar and contextual spelling checker that will make all grama nazi's obsolete. So my perspective upon this for intel is that I foresee no trouble for Intel, who already adapt to change and are not to be dismissed any time soon, just yet.
- jdjebc82747 10y ago>For me, I'm looking forward to a AI grammar and contextual spelling checker that will make all grama nazi's obsolete. I'm not. I feel like human language is meant to be fluid and to evolve as we do. This would potentially lead to more of a global monoculture than we are already starting to get.
- Zenst 10y agoThat is a very fair observation and one in which I had not considered. I was somewhat biased in thinking about the aspect that it would make universal translation closer to becoming a reality. So it is somewhat a chaos aspect in languages I suppose that does allow evolution of the language. With that the whole aspect could voice translation add's another aspect to this and perhaps tackling keyboard inputs upon a keyboard layout that is designed to be the worst possible ever layout is an area due to die off sooner than we think. Though the prospect of forcing generations to endure, even if they do not know about typewriters does bemuse me.
- jcranmer 10y agoIn the Classical era, grammar study was focused on a few idealized languages (Latin, Greek, Sanskrit); when people in the Early Modern decided to apply these rules to vernaculars, they ran into the problem that many modern languages don't follow such clean rules. The most common response was to try to insist that things that didn't look Latin shouldn't be considered "grammatical." Only quite recently did people begin working out how to describe the grammar of languages like English. One consequence is that almost everything you're taught about English in school is completely and totally wrong. There's the completely bogus prescriptivisms that have no grounding (e.g., thou shall not end a sentence with a preposition). But even basic things like "what are the parts of speech" are pretty much wrong, being derived primarily from "this was what this Latin scholar said 2 millennia ago about a language that has a distant linguistic relationship to English." It's also worth pointing out that the trend to conserve spelling and the written form probably obscures the underlying grammar as it changes. The French clitic pronouns seem rather more like recently-introduced inflections to the verbs rather than clitics, and the 's of English acts rather more like a particle than a genitive case marker.
- abhianet 10y ago> But the GPUs also have new destinations: notably data centres where artificial-intelligence (AI) programmes gobble up the vast quantities of computing power that they generate. Should not it be "programs"? Or is "programmes" used in some dialect of English I am not aware of?
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- varelse 10y agoCrazy idea: buy the rights to sell AMD's Vega GPUs fabbed out of Intel and use Intel's resources to build top-notch math and AI libraries for them. Stupid idea: Keep insisting that x86-compatibility is the killer feature for winning the parallel processor wars. Stupidest idea: CS professors continuing to tell their students that learning concurrent programming is too hard.
- kogepathic 10y ago> Crazy idea: buy the rights to sell AMD's Vega GPUs fabbed out of Intel 1) I don't think Intel has a lot of spare fab capacity. Certainly not on the nodes AMD is looking to produce Vega on. 2) Intel only just announced a deal to start manufacturing ARM chips on their fabs. [0] Honestly I can't believe it took Intel so long to wake up and realize that their x86 business is okay, but if they want to survive long term they have to accept that they need another business segment to bring in money after x86 stops being as relevant as it is today. Just look at TSMC [1] if you want an example of why Intel is foolish to think they can keep being top dog with only x86. TSMC was nobody in the 90's, and now their market cap is within ~10% of Intel's [2] (TSMC @ 160B versus Intel @ 175B). TSMC doesn't even design their own chips. I'm not saying building semiconductors is easy, or that TSMC has no R&D costs, but you're talking about a company which specializes only in manufacturing some of the most advanced chips on the planet, and doing it at volumes I doubt Intel can match. I predict unless Intel does something major in the near future (<24 months), TSMC will surpass Intel's market cap. The former CEO of Intel Paul Otellini captured it best himself: "It wasn't one of these things you can make up on volume. And in hindsight, the forecasted cost was wrong and the volume was 100x what anyone thought." [3] Intel still thinks they can kill it by selling expensive CPUs. TSMC is proving that thinking is outdated. You don't have to have a 60%+ margin on your chips, you just have to make it up in volume. Where do you think the next billion chips are going to be sold? It's not going to be $500 x86 CPUs. It's going to be <$5 ARM chips in embedded devices, and that's exactly the market segment TSMC is appealing to. [0] http://www.theverge.com/2016/8/16/12507568/intel-arm-mobile-chips-licensing-deal-idf-2016 http://www.theverge.com/2016/8/16/12507568/intel-arm-mobile-... [1] http://www.google.com/finance?q=NYSE%3ATSM http://www.google.com/finance?q=NYSE%3ATSM [2] http://www.google.com/finance?q=NASDAQ%3AINTC http://www.google.com/finance?q=NASDAQ%3AINTC [3] http://www.theinquirer.net/inquirer/news/2268985/outgoing-intel-ceo-paul-otellini-says-he-turned-down-apples-iphone-business http://www.theinquirer.net/inquirer/news/2268985/outgoing-in...
- jhj 10y agoThe "3,854 cores" versus "28 cores" is dubious as always. 3,854 I think counts just the individual fp32 ALUs; a true similar comparison would be number of warp schedulers or maximum number of warps resident at once, or even just SM count (which share a cache). Apples to oranges (a super-hyperthreaded 1024/2048-bit wide vector machine with minimal cache to a minimally hyperthreaded 128/256-bit wide vector machine with lots of cache).
- astrodust 10y agoApples to oranges? It's more like how a hundred thousand squirrels can't write a novel no matter how long they're given but one person can given a few months. Not all compute devices are equivalent and "core" vs. "core" is a totally absurd comparison.
- deepnotderp 10y agoFounder of a similar startup here. The strategy that nervana is taking is to reduce precision to 16 bit fixed point and then accumulate in 48 bits (which appears to be unnecessary and 24 bits should be sufficient). I can answer any questions if anyone has any.
- lowglow 10y agoYeah, what's a good intro on understanding all of this? I've got an EE/Chem/Math background.
- deepnotderp 10y agoFor deep learning or chips for deep learning? With an EE/Chem/Math background you should be set to go :) For deep learning, I highly recommend the cs231n course materials (available for free online) and the Deep Learning Book by Goodfellow et al. For chip design for deep learning specifically, it's a fairly new field, and a lot of commercial interests, hence why a lot of it isn't available in the form of an "Intro to Deep Learning Chip Design" course. But, the basic point is that deep learning can both train and perform inference in astonishingly low precision. For training: https://arxiv.org/abs/1502.02551 https://arxiv.org/abs/1502.02551 For inference, there are so many papers confirming this fact that there really isn't one key paper to point to. This article is a good introduction however: https://petewarden.com/2015/05/23/why-are-eight-bits-enough-for-deep-neural-networks/ https://petewarden.com/2015/05/23/why-are-eight-bits-enough-... Other than that, stripping out the cache hierarchy, HBM memory, etc. are the obvious steps to take in targeting deep learning. Note that it's my opinion that deep learning chip startups (such as ours) need another "secret sauce" beyond simply lowering precision, we're not like Intel which can just say "okay, here's $100mil, make me an 8-bit GPU, Go."
- lowglow 10y agoThanks! Mind if I connect with you over email?
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- phkahler 10y agoDo most of these application (machine learning, vision, etc) rely on OpenCL? It seems to me that GPUs are better suited to OpenCL than a regular CPU, but if that's what all the excitement is about I suggest reading up on some of the work on adding vector extensions to RISC-V and the corresponding flops/watt they're may achieve. They are basing some of the work on results from here: http://hwacha.org http://hwacha.org although they make it clear that hwacha will not be the standard vector instruction set.
- TazeTSchnitzel 10y agoIntel must be regretting dropping their dedicated GPU project.
- modeless 10y agoThey didn't exactly drop it. It's the Knights series (Xeon Phi). They just removed the graphics bits.