
Intel Announces Knights Mill: A Xeon Phi for Deep Learning - scaz
http://www.anandtech.com/show/10575/intel-announces-knights-mill-a-xeon-phi-for-deep-learning
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cs702
We badly need an alternative to Nvidia/CUDA for deep learning... but
realistically, if Intel wants to make headway in the deep learning market, it
must offer hardware that can not only compete on performance with Nvidia, but
also work out-of-the-box (that is, without requiring lots of one-off tinkering
and tweaking) with popular deep/machine learning frameworks like TensorFlow,
Caffe, Torch, and Theano.

There is a lot of software infrastructure being built atop these frameworks,
and switching costs are getting higher by the day. No one wants to use some
kind of 'non-standard' fork of [name your DL framework of choice] customized
for Intel hardware, because such a fork can quickly get stale in comparison to
the upstream project.

Intel needs to be both better/faster _and drop-in compatible_ with the popular
frameworks.

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jlebar
Tensorflow has a mode that lets it run on CPUs. I'm sure other frameworks are
the same. Isn't the whole point of Xeon Phi that it looks basically like an
x86 CPU with a ton of cores? If so there is almost nothing to port, just the
kernel launching.

Granted, you do have to bother to make a fast x86 / AVX-512 port of your code.
But because the shape of GPUs is so different than CPUs -- GPUs have a more
complicated memory hierarchy for one thing -- I kind of doubt that "just run
your CUDA code in the Phi" is going to work well for nontrivial examples.

(Disclaimer, I work at Google on CUDA support in clang. Which is awesome, you
should try it out. :) Google "cuda clang" for instructions.)

~~~
jlouis
To elaborate: Tensorflow is clever because it takes your flow graph and cuts
it into pieces which it then executes on computational units. Any
computational unit will do, really as long as you have a backend for it. This
choice makes adaptation to new system much easier.

I believe Caffe and Theano uses the same model, but I didn't study it. There
are also some similarity in the model to what OCaml does with the incremental
library, though it is not for machine learning.

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filereaper
This earlier thread on HN might be of interest:

Why didn't Larrabee fail?
[https://news.ycombinator.com/item?id=12293308](https://news.ycombinator.com/item?id=12293308)

~~~
kartD
Not a very good one, I feel it covers up too much of Larabee's past. I think
it was well established that it would be a GPU, but suffered from Intel
getting confused about what it should be.

If anyone would like to know more about Intel, I think this AMA is much better

[https://www.reddit.com/r/IAmA/comments/15iaet/iama_cpu_archi...](https://www.reddit.com/r/IAmA/comments/15iaet/iama_cpu_architect_and_designer_at_intel_ama/)

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cordite
Will there ever be an ARM coprocessor with several hundred nodes available?

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kartD
Nice, but how is this going to fit with Nervana?

Also more than hardware how do Intel's libraries compare with CuDNN?

At the end of the day ease of use and software support matter along with the
hardware.

~~~
zump
Dude, they bought Nervana like yesterday.

Nervana has its own silicon, but I doubt they will tape out.

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frozenport
Intel has thousands of employees and a compiler. Get 200 of them in a room and
implement CUDA.

~~~
visarga
They don't even have to implement the latest flavor of "Inception module",
they only need to implement matrix vector operations and some math primitives
like exponential, log, tangent and such. Why is it so hard to port to Intel? I
would have liked to make use of my Macbook's Intel Iris GPU for deep learning,
but it's not supported by anything.

~~~
nl
OpenCL supports Iris.

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ThinkBeat
I wish they had a range that was affordable for a hobbyist. You can buy a
cheap Nvidia card to "get your feet wet".

I would like to play with these things.

~~~
imaginenore
Why not use Google's platform, which runs on their new custom chips (Tensor
Processing Unit)?

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vonnik
Why use it, if it locks you in?

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xadhominemx
If you're a hobbyist, why would you care about lock in?

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flamedoge
Because you don't have control over what Google does. They may kill TPU
altogether leaving your work irrelevant.

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willvarfar
You don't have control over what nvidia, intel or the rest of them either.

If you want to get your 'feet wet', then why bother?

~~~
flamedoge
Because they have client facing products and backward compatibility is
important to clients.

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zump
How much?

