Machine learning, one of the most revolutionary emerging
technologies, has dramatically reshaped the landscape in different
fields and completely transformed the way we approach complex tasks.
Artificial neural networks (ANNs) form the foundation of modern
machine learning techniques. The training of ANNs, however, requires a
massive amount of computation that is both time and energy consuming,
and quickly saturates the available computational power. This poses a
severe challenge to the electronic computation architecture, especially
with the declining of Moore’s law. A possible solution is offered by
optics, which is a natural platform for linear computing thanks to its
coherence and superposition properties.
Although optical neural networks (ONNs) have been investigated for a
long time, optical training of neural networks has never been realized.
The training of neural networks involves (1) signal forward propagation;
(2) loss function calculation;
(3) error backpropagation and
(4) weight parameter update.
While the first two steps can be implemented with optics in different
ways, the backpropagation step (3) is extremely challenging since it
requires a) different responses of the nonlinear unit in forward and
backward directions and b) derivative calculation of the nonlinear
function. This optical backpropagation problem has been hindering the
development of ONNs for decades.
Recently, we discovered that
optical backpropagation can be realized by adopting the simple
pump-probe scheme with saturable absorbers. With forward-propagating
signal and backward-propagating error taking the roles of strong pump
and weak probe beams, all the requirements of backpropagation are
satisfied. Although optical backpropagation is an outstanding challenge
to this field, our solution is surprisingly simple and effective, and it
allows us to build ONNs with end-to-end optical training for the first
time. As we found through numerical simulation in image classification
tasks, our ONN can reach state-of-the-art performance with practical
experimental parameters.
In this proposed research project, I aim to realize the first
optically-trained ONN with the optical backpropagation scheme. The ONN
will be constructed with free-space optics and atomic vapor cells, and
it will be benchmarked in image classification tasks. The long-term goal
is to build ONNs that can outperform current electronic ANNs.
