Neural networks are
a type of AI system
that can be used
as a powerful tool
to predict patterns in data,
images, text, or video.
However, neural networks
tend to be extremely large,
making them inefficient to train
and deploy on small devices
like robots and phones.
Thus neural networks usually
require expensive cloud
infrastructure, like GPUs.
Graduate students at MIT CSAIL,
Lucas Liebenwein and Cenk
Baykal and several
other colleagues,
aim to take a large
neural network
and reduce the amount of parts
into a smaller architecture,
allowing it to be deployed onto
smaller devices like robots
and phones.
Our work is also backed up by
our accompanying mathematical
theory that enables
you to predict
how much pruning can be done.
In some sense, it opens up
the black box of deep learning
by analyzing each network
component individually.
To the best of our
knowledge, actually, this
is the first time that
there's rigorous theory
for understanding how pruning
affects neural networks,
and specifically how
pruning affects the accuracy
of neural networks.
The algorithm they're using
is most useful for scenarios
for real-time
computing, where you
want the most accurate
predictions within the smallest
time frame possible.
With the smaller
architectures, you're
able to design cheaper
devices/infrastructures
and make more
efficient energy chips.
We don't need to rely on
an internet connection
or the cloud in order
to produce an answer.
Instead, we can guarantee
execute the neural network
on the device itself.
That makes it much
more fault tolerant.
In addition, by keeping the data
security on the device itself,
you can alleviate many of the
privacy concerns surrounding
AI, because otherwise
the data would
have to be sent to the cloud,
where it might be processed
unencrypted, or third parties
could gain access to it.
For robot systems,
in particular,
sending data to the cloud
is not a viable option.
Robots need to interact
with their environment.
They need to react to
changes in their environment.
And they need to do so with
minimum time delay possible.
So sending data to the
cloud could cause a delay
in the system, which could
cause catastrophic consequences
in turn.
So by using our
algorithms, now we
can instead directly execute
the neural network on the robot
and use the neural network
for tasks that might otherwise
be too computationally expensive
for the robot system to run.
There is still so much to
learn from neural networks.
And Lucas and his
group are hoping
to contribute tools that
can improve our high level
understanding of them.
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