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.
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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.
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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.
Only the PDP-10 has been replaced by Google.
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.
And, well, you know, “words”... whatever works :-D
Back in the 1980s, a personal computer was loaded with an operating system that could only support a single user. A "mainframe" was a system that could support more than one user at a time, and you'd connect to it using telnet.
Today, your Linux PC loaded with SSH effectively functions as a mainframe. Heck, you can load virtual machines that themselves can pretend to be mainframes from the 1970s. Heck... your cell phone uses this feature and pretends that apps are different users for maximum security.
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In contrast: GPUs are simply the 1980s style SIMD super-computers. It turns out SIMD Supercomputer style was the fastest way to calculate where pixels go on the screen (Conceptually: each pixel-shader basically gets to run its own thread: so a 1920x1080 screen has 2073600 threads to run per frame)
The best architecture to run millions of simple threads is a GPU or SIMD-computer (See https://en.wikipedia.org/wiki/Connection_Machine)
> The CM-1, depending on the configuration, has as many as 65,536 individual processors, each extremely simple, processing one bit at a time.
May be a terminology thing, or a technology I don't know, but I though '80s supercomputers like crays were vector processors, whereas I'm not aware of any commercial super at the time being SIMD (AKA array processor).
A vector processor as I understood it is nicely described here <https://www.quora.com/What-is-meant-by-an-array-processor-an... as "the earliest Crays had vector instructions, that quickly fed a stream of operands through a pipelined processor", which is not exactly the same implementation as SIMD, but after reflecting on your question, it does the same thing in the end.
So effectively no difference.
Also relevant <https://arstechnica.com/civis/viewtopic.php?t=401649> see reply of Accs who claims to have worked at cray, and also says in another post on the same thread "I think that Vector is nothing more than a special case of SIMD".
So, my error, hope above helps.
FYI you might want to read up on systolic arrays just for fun <https://en.wikipedia.org/wiki/Systolic_array>.
@tntn: thanks, my carelessness.
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.
I have a feeling that AI technology is inherently anti-privacy.
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
They still store audio when you use Ok Google though.
https://ai.googleblog.com/2019/03/an-all-neural-on-device-sp...
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) 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.
P.S. Chuck, you are a great inspiration and friend. Thank you for all the kindness you've shown me.
https://www.theverge.com/2019/2/4/18209546/google-live-trans...
Disclaimer: I work in Google, but apparently have never touched any of this from the inside.
Voice recognition without the cloud has been possible for years, this is nothing new.
> 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.
> ...it just happens that most implementations today are...