
All-optical machine learning using diffractive deep neural networks - orbifold
http://science.sciencemag.org/content/early/2018/07/25/science.aat8084
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AndrewGYork
I asked this on Twitter, but maybe folks here can answer better: how important
is nonlinearity for deep neural networks? This method's output seems to be a
linear function of its (complex) input. Does that put important bounds on
performance?
[https://mobile.twitter.com/AndrewGYork/status/10228414045888...](https://mobile.twitter.com/AndrewGYork/status/1022841404588883968)

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czr
Echoing the other respondents–if you don't have a nonlinearity, your whole
network is just a sequence of linear transforms, which (multiplied out) is the
same as a single linear transform. Meaning that removing the nonlinearities
gives you (effectively) a one-layer network.

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orbifold
There is an arxiv version of the paper at
[https://arxiv.org/abs/1804.08711](https://arxiv.org/abs/1804.08711).

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jboggan
This is really important if it can be scaled up. Imagine being able to replace
an onsite heavy compute core pulling several hundred watts (like in the trunk
of a Waymo car) with a passive unpowered optical element. Pretty amazing.

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dekhn
I'd just like to point out that back in the 60s there was this physical neural
network called the Perceptron. Yeah, like the ones you learn about. However,
it was a patchpanel machine (the wires were the neurons) with weights that
were implemented as potentiometer knowbs attached to little servos. It could
do backprop (IIRC it was a single layer) and basic image recognition.

Some days I feel like neural network hardware is the new laser: at one point,
nobody thought it could exist, but once one was made, new designs started to
fall out of the woodwork. Like gravitational lenses, there are actually
"galactic laser foundries" that generate lasers purely out of stellar physics.

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TeMPOraL
The title doesn't do the article justice. This is a _physical, 3D-printed_
neural network, in form of plates that pass/reflect light. You shine your
input at one end, get results at the other.

I'm very impressed. Not sure if this has any chance of being more efficient
than traditional NNs implemented in silicon, but I can imagine some fun
applications. For instance, with some optics in front, I think it could be
used as a passive classifier of what's in front of the detector - you could
set up an array of photodetectors in the back, that operate a low-power device
only when appropriate pattern is detected.

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dekhn
I don't think this is correct. I think you have to provide a light source for
this to work, so it's not low power.

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TeMPOraL
I was thinking of sunlight as the source, with optics focusing the image.

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andrewjrangel
Can someone help me Grok how the 3D printed Neural Networks back propagate? As
I am trying to go through the paper they describe it as a pure optical
approach, but what adjusts the refraction elements during the learning
process?

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flohrian
from the paper:

"[...] learnable network parameter that is iteratively adjusted during the
training process of the diffractive network, using an error back-propagation
method. After this numerical training phase implemented in a computer, the
D^2NN design is fixed and the transmission/reflection coefficients of the
neurons of all the layers are determined. This D^2NN design, once physically
fabricated using e.g., 3D-printing, 3lithography, etc., can then perform, at
the speed of light propagation, the specific task that it is trained for,
using only optical diffraction and passive optical components/layers, creating
an efficient and fast way of implementing machine learning tasks."

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andrewjrangel
So the optical component is only the end result model of the NN? It isn't
learning using the optics?

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dekhn
Only indirectly. the physical device only does feedforward so they had to
train it using tensorflow on a conventional device.

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cheeko1234
Check out this new article:

[https://www.osa.org/en-
us/about_osa/newsroom/news_releases/2...](https://www.osa.org/en-
us/about_osa/newsroom/news_releases/2018/researchers_move_closer_to_completely_optical_arti/)

They implemented a back propagation algorithm using just optical.

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lallysingh
You can almost hear the truckloads of grant money driving to their office.

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deepnotderp
This has no nonlinearities, this isn't a neural network, it's a linear
classifier...

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bra-ket
related: "Researchers Move Closer to Completely Optical Artificial Neural
Network"
[https://news.ycombinator.com/item?id=17730775](https://news.ycombinator.com/item?id=17730775)

