
Intel Announces Movidius Myriad X VPU, Featuring ‘Neural Compute Engine’ - mcone
http://www.anandtech.com/show/11771/intel-announces-movidius-myriad-x-vpu
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jhallenworld
One interesting thing about these Myriad chips is that they use Leon- an open
source SPARC-V8 core for their main and "real time" CPUs. It's odd to me that
it's not using ARM...

[http://eyesofthings.eu/wp-
content/uploads/deliverables/EoT_D...](http://eyesofthings.eu/wp-
content/uploads/deliverables/EoT_D2.3.pdf)

~~~
fra
Before they were Intel, Movidius was a small silicon company. They probably
just didn't want to pay the ARM licensing fee.

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TazeTSchnitzel
> 4K at 30 Hz (H.264/H.265) and 60 Hz (M/JPEG) supported

I am greatly amused at the thought of 2160p60 MJPEG. That must be extremely
efficient…

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cmrdporcupine
I chuckled at that, too. I struggle to imagine what would be on the receiving
end of that. I doubt most desktop machines could decode a 60fps 4k MJPEG
stream, let alone a phone. Maybe they sell an SOC the other side too.

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yeureka
Any desktop would be able to do so.

I wrote a multi stream mjpeg decoder some years ago and it could run three
MJPEG streams at 1920x1080@30 on a 2008 Macbook Pro (Core 2 Duo, 2.5ghz ).

~~~
userbinator
That's only ~187MP/s. A single 4k 2160p60 stream needs almost 500MP/s, which
might be at the edge of what's possible on a CPU today, but would make far
more sense for a GPU to do.

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wtallis
It shouldn't be a stretch for a quad-core desktop processor today. Doubling
the core count and increasing clock speed by 40% compared to a mobile Core 2
isn't hard. DDR4 instead of DDR2 means memory bandwidth is probably not an
issue, and AVX can probably provide further headroom on the compute power.

And, of course, it's much easier to build a desktop with far more than four
CPU cores these days.

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occultist_throw
I couldn't trust it. Not that I know that product specifically, but given
previous working with Intel products outside of their X86 line, it will likely
be EoL'ed in a year or so.

Ive been bitten by a few of their "Maker things". Faulty firmware, no support,
no documentation, faulty I2C.. All sorts of things. And unless I have a
timeframe how long they plan to support it and sell it, I just can't justify
using it in any product I have or plan to build.

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mv4
I was just going to comment on that. Just recently, Intel quietly killed
Edison, Joule, Galileo.

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agumonkey
These aren't the same crowd though. NN/ML is new, hot, technical .. SBC and
embedded on the other hand ..

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pjmlp
Yet it is the same management board.

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schappim
Intel making neural compute engines isn't anything new. They've previously had
them within the Intel Curie chips, such as those found on the Arduino 101.

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MrBuddyCasino
Yeah, but nobody aver seemed to figure out what to use them for it seems.
Probably too limited in its capabilities (128 neurons with 128 bytes each
imho).

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pyladune
do that really brings power ??

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valarauca1
If this is anything like the Xeon Phi its Intel trying to push their own
Float16 standard. We all saw how well Float80 worked out when will Intel stop
with arbitrary custom standards.

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scottlegrand2
Not to defend Intel because I think Intel has become a Ship of Fools With a
lot of money, but AMD has FP24, Nvidia has FP16 and Google has TPU 2 with an
FP16 implementation so proprietary they can't talk about it. I think the valid
concern here is that if this doesn't take off immediately they will end-of-
life it and not so much the floating point format.

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borramakot
I won't defend FP24, but FP16 is a widely supported IEEE standard.

[https://en.wikipedia.org/wiki/IEEE_754](https://en.wikipedia.org/wiki/IEEE_754)

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dogma1138
Not exactly, IEEE 754 defines very specific rules (e.g. what operations can be
done, order of operations and how the results are handled), it's basically
exists so that if the same calculation is done on different hardware the
result will be the same (or within a given margin of error since even IEEE 754
isn't 100% strict).

For the most part when you have hardware that supports one of the binary
standards say FP32 you'll have a compliance sheet, more often than not it will
not be 100% compliant for example NVIDIA GPUs were not IEEE 754 compliant when
Tesla came out, 2nd Generation Tesla cards were FMA compliant, div and sqrt
operations were not IEEE 754 compliant until Fermi.

So the GP was correct, back in the old DX9 days when AMD went with FP24 and
NVIDIA with FP16/32 both implementations were proprietary, and this is still
the case, they just often offer a 754 compliant mode. You can disable 754
compliance to run faster (sometimes considerably so) e.g. in CUDA you can use
the NVCC flag --use_fast_math to do so.

