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Arm announces its new premium CPU and GPU designs
- ChuckMcM 7y agoThis is interesting to me, lots of noise around "machine learning" in the GPU rather than graphics which kind of validates Google's TPU, Apple's AI co-processors, and Nvidia's Jetson. Saw a system at Makerfaire that does voice recognition without the cloud (MOVI) and I keep thinking having non-privacy-invading computers you could talk to would be a useful thing. Perhaps ARM will make that possible.
- dmix 7y agoHow much data would it require for a real-world voice "model" that is locally on a device, space-wise? I don't know much about machine learning's actual implementations within software, I've always been curious how you go from (say) Tensorflow -> embedded within real world software / APIs.
- baddox 7y agoIt doesn’t exactly answer your question, but Apple has a decent description of how local “Hey Siri” detection works on the iPhone and watch: https://machinelearning.apple.com/2017/10/01/hey-siri.html https://machinelearning.apple.com/2017/10/01/hey-siri.html
- NotPaidToPost 7y agoThe limiting factor is generally not memory but processing and power consumption.
- dmix 7y agoSure, my question was what the local algorithm/dataset looks like packaged up.
- cpeterso 7y agoEarlier this year, Google introduced an Android "Live Transcribe" feature that runs locally without the need for an internet connection: https://www.theverge.com/2019/2/4/18209546/google-live-transcribe-sound-amplifier-accessibility-android-deaf-hard-hearing https://www.theverge.com/2019/2/4/18209546/google-live-trans...
- kyriakos 7y agoAnd it's very accurate. I tested it with English and Greek for which usually dictation systems are very bad and was surprised how well it recognised every word.
- lrem 7y agoGoogle Translate offline files seem to be about 50MB per language. Assume your speech recognition machinery breaks down utterances into phonemes, alphabet a couple times that of letters and some more entropy... My intuition is you should be in the 200-500MB range. Disclaimer: I work in Google, but apparently have never touched any of this from the inside.
- neysofu 7y agohttps://github.com/Picovoice/Porcupine https://github.com/Picovoice/Porcupine
- mycall 7y agoNvidia's Jetson really is exciting what it can achieve.
- HeWhoLurksLate 7y agoAs are AMD's newest announcements of Navi & Intel-smacking performance- they could probably lean over this way and jostle things around quite a bit if they wanted to.
- keerthiko 7y agoI would hope at least chip designers/manufacturers will stop referring to chips designed and marketed specifically for AI/ML as "G"PUs, when they aren't really about graphics processing anymore.
- dmitriid 7y agoGeneral Processing Units then? :)
- rangibaby 7y agoThat's very clever, and reminds me of DVD (Digital Video Disc to Digital Versatile Disc)
- nightcracker 7y agoIf a CPU is a Central Processing Unit then I guess we should call them DPUs: Distributed Processing Unit.
- pavlov 7y agoMaybe “Parallel Processing Unit”, PPU?
- p1mrx 7y agoGuided processing unit. The CPU tells it what to process.
- 7y ago
- ajxs 7y ago> non-privacy-invading > computers you could talk to Pick one. I can't help but feel somewhat pessimistic about technological developments in the handheld device domain. I say this as a supporter of ARM though. I have no reason to dislike ARM, I'm just somewhat apprehensive regarding any more empowerment of the companies we keep in our pockets.
- Majestic121 7y agoTalinkg to a computer is an interface like any other (mouse/keyboard). There's no fundamental reason why talking to a computer would be inherently privacy invading, it just happens that most implementations today are, for technical/performance reasons.
- ajxs 7y agoOf course, I understand what an interface is. There's nothing inherently invasive about this kind of interface, except that... > ...it just happens that most implementations today are...
- jaytaylor 7y agoWere you able to try it out yourself? Curious how good it is compared to "OK Google". P.S. Chuck, you are a great inspiration and friend. Thank you for all the kindness you've shown me.
- pavlov 7y agoIt’s interesting that the cycle is complete and we’re back to the 1975 situation: “Wouldn’t it be great to have a personal computer that could do all these things we currently do by connecting to a mainframe?” Only the PDP-10 has been replaced by Google.
- amelius 7y agoExcept back then the driver wasn't privacy.
- pavlov 7y agoYes, it’s more like a cycle of reincarnation: the same problems and solutions keep coming back in different guises.
- HeWhoLurksLate 7y agoAnd those problems and solutions are also somewhat caused by / solved by more modern technology.
- dahfizz 7y agoIs privacy the only driver? I think convenience, speed, and cost are still big concerns, just like they were back in the day. Network requests are always very slow when compared to processor speed. You can buy a device for 1/500 the cost of a server, put it in your closet, and it will be faster at almost every task than offloading to the "cloud". I work tangential to the Telecom space, and all anyone cares about is the "edge" i.e. Consumer Premise Equipment and the like. With how cheap computer power is and how high network latency is, having centralized datacenters has become less and less desirable.
- Demiurge 7y agoIt is a wave however, rather than a circle. There are oscillations, back and fourth between all sorts of things. It is inevitable because the competing ideas and systems are oscillating, all the way down. Nature is not perfectly in sync, and that is indeed what makes things interesting.
- freyr 7y agoGoogle is rolling out on-device voice recognition too. They’ve integrated it into Gboard: https://ai.googleblog.com/2019/03/an-all-neural-on-device-speech.html?m=1 https://ai.googleblog.com/2019/03/an-all-neural-on-device-sp...
- dragontamer 7y agoGPUs are the 1980s style SIMD compute system. A GPU is designed to run many, many, many threads. A Vega64 for example, can run 163,840 threads at the same time (4096 shaders x 10-way SMT x 4-threads per unit)... while a CPU Threadripper 2950x can "only" run 32 threads at once (16 cores x 2-way SMT). However, the GPU-shader is far more constrained than a CPU-thread. A GPU-shader is "locked" to performing the same tasks as other cores. That is to say: 64 "GPU threads" on AMD systems have the same program counter / instruction pointer. 32 "GPU threads" on NVidia systems have the same program counter. This means that if one thread loops 10,000 times in a program, then that will force 32 other threads (on NVidia) or 64 other threads (on AMD) to loop 10,000 times (even if those threads individually only need to loop 500 times). This restriction doesn't really matter in cases of matrix multiplication: where all threads would loop the same number of times. But in cases of like, Chess Minimax algorithm, the "thread divergence" makes it difficult to actually port Chess Minimax to a GPU. ------- Machine learning... (EDIT: or more specifically, Convolutional Neural Networks) is almost purely a matrix multiplication problem, making it ideal for the SIMD GPU architecture. CPUs have SIMD units by the way (SSE, AVX, etc. etc.), but GPUs are specialized systems that can only do SIMD. So a GPU's SIMD unit is inevitably going to be bigger, more parallel, and more efficient than a CPU's SIMD unit. ------- EDIT: Google's TPU goes beyond the GPU-model (SIMD architecture) and has dedicated matrix-multiplication units. These units can only perform matrix multiplication, nothing else, and are designed to do it as quickly and power-efficiently as possible. NVidia's "Turing" line of GPUs have dedicated matrix multiplication units called "tensor units". NVidia basically grafted a dedicated FP16 matrix multiplication unit onto their GPUs.
- The_rationalist 7y ago"So a GPU's SIMD unit is inevitably going to be bigger, more parallel, and more efficient than a CPU's SIMD unit." Except if memory latency transfer is a bottleneck. I've seen many examples of SIMD beating GPU e.g pathfinder v3 text renderer
- slaymaker1907 7y agoWhile memory latency transfer between the CPU and GPU is an issue, GPUs have absolutely incredible throughput when moving things from one place on the GPU somewhere else on the GPU. This is why researchers are looking at GPUs for running graph algorithms.
- vbezhenar 7y agoWhat I always wonder is that AI requires data to learn from. When Google sends everything to their servers, they have that data from all over the world. So they can build better AI. If their competitors implement privacy-respecting solutions, they are doomed to be behind Google, because they don't have data to learn their AI. My personal anecdote: my speaking English is quite bad and Apple Siri does not understand me well. But Google Assistant does wonders, it's fascinating how well its speech recognition works. I have a feeling that AI technology is inherently anti-privacy.
- ourlordcaffeine 7y ago>Saw a system at Makerfaire that does voice recognition without the cloud Voice recognition without the cloud has been possible for years, this is nothing new.
- gok 7y agoThe recent ML-focused chips are really just high efficiency, low precision matrix multipliers. It turns out that operation can be made ~10x more efficient than previous processor designs, and is the bottleneck in inference for modern neural network models. Specialized hardware can also accelerate the training somewhat. So having this hardware in your phone means that, for example, face recognition can run faster, and scene analysis during video recording takes less battery. But the neural network inference speed is rarely the issue that requires both running models on servers and collecting user data. In your case of speech recognition, the problem is that training state of the art models requires 10s of thousands of hours of speech data. A recent Amazon paper actually used a million hours. For language models you need even more. Modern phones actually can perform speech recognition locally (put your phone in airplane mode, dictation still works) but it's using models trained using data from users that did talk to the server.
- shpx 7y agoGboard (Google's keyboard on Android) has had voice recognition without the cloud for three months now, no extra work from ARM needed. https://techcrunch.com/2019/03/12/googles-new-voice-recognition-system-works-instantly-and-offline-if-you-have-a-pixel/ https://techcrunch.com/2019/03/12/googles-new-voice-recognit... Looks like they're not uploading clips to themselves either, at least it doesn't show up in https://myactivity.google.com https://myactivity.google.com They still store audio when you use Ok Google though.
- fpgaminer 7y ago> Saw a system at Makerfaire that does voice recognition without the cloud (MOVI) and I keep thinking having non-privacy-invading computers you could talk to would be a useful thing. Perhaps ARM will make that possible. Yes, powerful ARM embedded platforms are making this more and more possible and I'm at least excited about it. Not only that, speech recognition and speech synthesis technologies are getting more efficient by the month. There was a paper just this past week (https://arxiv.org/pdf/1905.09263.pdf https://arxiv.org/pdf/1905.09263.pdf) which demonstrates speech synthesis with an order of magnitude faster performance. Advances like those make it easier to cram these technologies on local IoT platforms. And there's certainly been a number of local focused AI projects over the past few years. I remember Snips.ai being decent for building local-only voice assistants. It worked on a Raspberry Pi, though you still have to hack together decent microphones for it. All of this gives me hope for an open source voice assistant that will be competitive with the commercial options. Now if the makers of smart accessories (lights, etc) would stop being anti-consumer for two minutes we might get decent accessories that don't require a cloud.
- applecrazy 7y agoYou were at Maker Faire? Awesome, I'd love to hear your thoughts on the Faire in comparison with previous years.
- thomasfl 7y agoThe day Apple releases their first ARM powered laptop, will will be a turning point. This comment written on an ipad, the best product to come out of apple to yhis day.
- xvector 7y ago> out of apple to yhis day. I suppose keyboards haven't really been a strong point!
- imperialdrive 7y agoWhile I don't agree with the first part, I do agree that the iPad is unique and worthy being the only Apple device my hard earned money has ever gone towards.
- pjmlp 7y agoAlan Kay might disagree with its uniqueness.
- intricatedetail 7y agoWhich one do you have? I have iPad 2 and it still works great. Is it worth upgrading?
- amirmasoudabdol 7y agoSad autocorrection of "ARM" into "Arm" in the title on the text!
- saagarjha 7y agoArm is the company name: https://www.arm.com https://www.arm.com
- amirmasoudabdol 7y agoIt was always the company name, the logo is lowercase now, but the abbreviation should still be upper case.
- saagarjha 7y agoBut I don't think the article is using the abbreviation?
- deleted 7y ago[deleted]
- samcday 7y agoI thought they go by "Arm" now? Their website seems to indicate as much.
- otherflavors 7y agoIt is the evolution of the arm
- hyperpallium 7y agoThe chart has 4 data points, but only 3 CPUs. The Cortex-76 is in the middle. Can anyone explain? Also, in the wider socioeconomic picture, everyone once got the same computational technology eventually, because better was also cheaper. Now, computation is stratifying, like any normal industry, where you get what you pay for. This ending of egalitarianism is bad.
- blu42 7y agoOne guess re the second CA76 datapoint would be N1.
- hyperpallium 7y agoN1? please elaborate
- blu42 7y agoThe datacentre (AKA 'Neoverse') CA76 variant.
- clouddrover 7y ago> where you get what you pay for I think that's always been true for CPUs and GPUs. The faster ones were always the more expensive ones.
- hyperpallium 7y ago"eventually" the faster ones got cheaper, in previous times
- clouddrover 7y agoNo, there was just a new model of processor and the cheaper variant of it was faster than the previous model's cheaper variant. Things haven't changed in that regard.
- Causality1 7y ago>the company argues that 85 percent of smartphones today run machine learning workloads with only a CPU or a CPU+GPU combo. Gotta admit I'm not real clear on what my phone does that needs on-device machine learning.
- PhilippGille 7y agoAutomatic enhancements when taking a photo / video (digital zoom, anti shake, "night mode") for example.
- mceachen 7y agoApplying an existing, trained model (like what you described) is computationally cheap. Training new models takes many, many orders of magnitude more time and memory space to complete.
- jononor 7y agoMuch cheaper than training for sure. But inference for image classification it is generally in the order of hundreds of milliseconds when using CPU. That wont fly for real-time inference on video, for example. Which is very desirable for even "image" uses, because it allows for live preview in the camera app.
- bufferoverflow 7y ago2019 Google I/O presenters talked about it: much much better latency, no need to send the audio to the server for speech recognition.
- sixothree 7y agoBesides that data is no longer useful to them.
- bsaul 7y agoWas about to write the same comment. Plus, i must say every time i hear some function is going to be performed via a NN, i'm thinking that my device is becoming more and more random in the way its behave. Which i really don't like. At least when some classical algorithm fails at performing certain task, people talk of the failure as a bug. Not as something that's "probably going to be solved with more training data".
- lone_haxx0r 7y agoUnreadable. As soon as I click on the (X) button, the article closes and the browser goes back to the root page of https://techcrunch.com/ https://techcrunch.com/. There's no way to avoid clicking on it, as it follows you around and grabs your attention, not letting you read.
- mastazi 7y agoNot to mention that Techcrunch is far from being the best source in relation to hardware...
- founderling 7y agoEven when you fight that annoyance with a content blocker, the page itself is aggressive to no end. I scrolled down to see how long the article is. That somehow also triggered a redirect to the root. How is it possible that the most user hostile news sites often get the most visibility? Even here on HN which is so user friendly. What are the mechanics behind this? The url should be changed to a more user friendly news source. How about one of these? https://liliputing.com/2019/05/arm-launches-cortex-a77-cpu-mali-g77-gpu-and-arm-ml-npu.html https://liliputing.com/2019/05/arm-launches-cortex-a77-cpu-m... https://hexus.net/tech/news/cpu/130757-arm-releases-cortex-a77-cpu-next-gen-mobile-devices/ https://hexus.net/tech/news/cpu/130757-arm-releases-cortex-a... https://www.xda-developers.com/arm-cortex-a77-cpu-announcement/ https://www.xda-developers.com/arm-cortex-a77-cpu-announceme... https://www.theregister.co.uk/2019/05/27/arm_cortex_a77/ https://www.theregister.co.uk/2019/05/27/arm_cortex_a77/ https://venturebeat.com/2019/05/26/arm-reveals-new-cpu-gpu-and-machine-learning-processor-for-5g-world/ https://venturebeat.com/2019/05/26/arm-reveals-new-cpu-gpu-a...
- solarkraft 7y agoThanks! It'd be cool if HN had an alternative article feature.
- richeyryan 7y agoIf you click the "web" link at the top of the page, it brings you to a search for the post title. You should be able to find a number of alternate sources there
- narnianal 7y agoWhat even is an "AI chip"? What's the difference to a GPU? As long as nobody can explain that I have big doubts that it whould be more than a GPU+marketing. So no big deal if they don't provide one.
- lrem 7y agoI would guess a chip optimised for addition, multiplication and ReLU, not burdened too much by all those other rarely used opcodes.
- acidbaseextract 7y agoModern GPUs are extremely programmable, but this flexibility isn't that heavily used by neural networks. NN inference is pretty much just a huge amount of matrix multiplication. Especially for mobile applications (most Arm customers), you pay extra energy for all that pipeline flexibility that isn't being used. A dedicated chip will save a bunch of power.
- fxj 7y agoThis link gives a nice explanation of the google nn-processor aka TPU: https://cloud.google.com/blog/products/gcp/an-in-depth-look-at-googles-first-tensor-processing-unit-tpu https://cloud.google.com/blog/products/gcp/an-in-depth-look-... It boils down to: CPUs: 10s of cores GPUs: 1000s of cores NNs: 100000s of cores NNs have very simple cores (fused multiply-add and look-up table functions) but can run many of them in one cycle. FTA: Because general-purpose processors such as CPUs and GPUs must provide good performance across a wide range of applications, they have evolved myriad sophisticated, performance-oriented mechanisms. As a side effect, the behavior of those processors can be difficult to predict, which makes it hard to guarantee a certain latency limit on neural network inference. In contrast, TPU design is strictly minimal and deterministic as it has to run only one task at a time: neural network prediction. You can see its simplicity in the floor plan of the TPU die.
- endorphone 7y agoNeural network learning and inference primarily uses matrix multiplication and addition, usually at lower bit depths. You can do this on GPUs to great success and massive parallelism, however the GPU is more generalized than this so it takes more silicon, and more power. With a TPU/neural processor you optimize the silicon to a very, very specific problem, generally multiplying large matrixes and then adding to another matrix. On a GPU we decompose this into a large number of scalar calculations and it massively parallelizes and does a good job, but on a TPU we feed it the matrixes and that's all it's made to do, with very large scale matrix operations, with so much silicon dedicated to matrix operations that it often does it in a single cycle. Another comment mentioned cores and I don't think that's a good way of looking at it, as in most ways a TPU is back to very "few" but hyper-specialized "cores". There is essentially no parallelism in a TPU or neural processor -- you feed it three matrixes and it gives you the result. You move on to the next one.
- fxj 7y agoHow does this NNPU compare to the other vendors (Goolge TPU (3 TOPS?, Intel NCS2 (1 TOPS), kendryte RISC-V (0.5 TOPS), Nvidia Jetson (4 TOPS)? Can you use tensorflow networks out of the box like the others provide?
- bufferoverflow 7y agodel
- MzxgckZtNqX5i 7y agoWrong thread perhaps?
- holstvoogd 7y agohmm, I feel ARM performance is a bit like nuclear fusion. It's always the next generation that will deliver an order of magnitude performance increase. Yet some how ARM single core performance is still shit compared to x86. (No matter how much I hope and pray for that to change, cause x86 needs to die)
- wongarsu 7y agoNvidias single core performance is even worse, yet their GPUs sell great. Lots of workloads have no need for individual fast cores
- rwmj 7y agoI'm afraid you're simply wrong here. Single core performance of the (now sadly deceased) Qualcomm Amberwing was incredible, easily Xeon-like. And it had 46 cores per socket. The Cavium ThunderX2 also has great single core performance coupled with many more cores than you can get on an Intel chip. Apples iPhone cores are also supposed to be good. (Edit: These are ARM cores, but not ARM-as-in-the-company designs)
- holstvoogd 7y agoInteresting, I was mostly thinking about the ARM's own cores, but never _really_ looked into the broader ecosystem. From my personal experience, Apple chips are fast, but I can't really compare that to anything else. JS benchmarks match desktop performance, but that is not really what I was/am interested in. I've tried some 'cloud ARM-based metal servers' which promised to have similar performance as intel Atom cpu's basically, but they felt at least 10 times slower. So I gave up on the whole concept. But I guess the take-away is, those specific systems where slow not so much ARM in general. I mean, it does make sense that there is some scale difference between a Xeon core vs a core meant for mobile use now that I actually think about it ;)
- pjc50 7y agoIs this true for comparable TDP processors? Speaking as the owner of one of the few x86 Android phones, the Intel performance isn't better on small devices.
- sigmonsays 7y agocan someone explain to me why I want ML specific processor features or chips in my phone? I thought ML required massive amounts of data to be taught, most of which makes more sense in the cloud. Am I way off here?
- jaydj 7y agoCheck out federated learning. https://medium.com/tensorflow/introducing-tensorflow-federated-a4147aa20041 https://medium.com/tensorflow/introducing-tensorflow-federat...
- jopsen 7y agoFrom what little I understand models are built in the cloud, compressed and evaluated on your phone. Better hardware probably means less power drain and larger models. There was some cool stuff about this in Google I/O keynote.
- btown 7y agoTraining a model does require massive data and compute, but evaluating/using an already model (e.g. running a Hot Dog/Not Hot Dog classifier) can be done on mobile hardware. Accelerating this could, for instance, allow this to run in real time on a video feed.
- NegatioN 7y agoIt doesn't have to be for training the models. You can run versions of trained models locally on your phone. Having a dedicated chip will allow for more snappy calculation of those fancy snapchat filters, language translation, image recognition etc
- joshvm 7y agoAside from the runtime difference between training and inference, having on-device ML makes a lot of sense for other reasons. There can be more guarantees over data privacy, since your data can stay on-device. It also reduces bandwidth as there's no need to upload data for classification to the cloud. And that also may mean it's faster, potentially real time, since you don't have that round trip latency. This is not necessarily for phones. Lots of (virtually all?) low power IoT devices have ARM cores. There are plenty of environments where the cloud or compute power isn't available.
- KirinDave 7y agoDoes someone have a mirror of this article that doesn't seize up if you refuse to display ads?
- The_rationalist 7y agoWhen will Deep learning frameworks get a reality check and decide to support openCL/SYCL? All this hardware is useless silicone until then
- Joky 7y agoTensorflow has the XLA compiler for targeting accelerators. In practice the integration isn't necessarily amazing right now, but the plan is to make it much better and able to generalize more easily. Disclaimer: I work on the MLIR/XLA team.
- The_rationalist 7y agoThe openCL tensorflow issue is still open and no Google dev has shown interest.. Yet I'm curious about your progress on this and when this could target AMD / Intel / ARM
- ksec 7y agoI think it is worth pointing out this new CPU possibly landing in Flagship Android in late 2019 or early 2020 would still only be equal to or slower than an Apple A10 used in iPhone 8. Assuming Apple continue like they do in the past, drop iPhone 7 and moves iPhone 8 down to its price range. They would have an entry level iPhone that is faster than 95% of all Android Smartphone on the market.
- Liquid_Fire 7y agoIsn't the entry level iPhone also significantly more expensive than most Android phones?
- ksec 7y agoDepends on Brand, although that will most likely be true since Top 5 Android Phones maker are all Chinese except Samsung.