Aerospace & Defense Technology - June 2024 - 30

Tech Briefs
NRL Research Physicists Explore Fiber Optic Computing
Using Distributed Feedback
Researchers introduce a fiber-optic computing architecture based on temporal multiplexing and
distributed feedback that performs multiple convolutions on the input data in a single layer.
Naval Research Laboratory, Washington, D.C.
U
.S. Naval Research Laboratory (NRL)
researchers have outlined a novel
contribution in fiber optics computing
in a paper recently published in Communications
Physics Journal that brings
the Navy one step closer to faster, more
efficient computing technologies.
Optical computing uses the properties
of light, such as its speed and ability
to carry large amounts of data, to process
information more efficiently than
traditional electronic computers.
In collaboration with Sandia National
Laboratories and the University of
Central Florida, NRL is aiming to
increase processing speeds, reduce
energy consumption, and enable new
applications in fields such as data processing,
telecommunications, and artificial
intelligence.
" This paper marks a significant
advancement in optical computing, "
Temporal
Encoding
said Brandon Redding, Ph.D., a Research
Physicist from the NRL Optical Sciences
Division. " It is the first to employ distributed
feedback in optical fiber, combining
temporal encoding with low-loss,
partially reflective fiber. Our approach
offers scalability to process multiple neurons
simultaneously, along with highspeed
performance and a compact, lightweight,
and power-efficient design, as
the entire system is fiber-coupled and
does not require free-space optics. "
The Navy is rapidly adopting machine
learning algorithms for a wide range of
applications. Many of these applications
are time and energy-sensitive. For
instance, image or target recognition
tasks where objects require identification
in real time.
" Many of these applications involve
forward deployed, often autonomous
platforms with limited power availabiliRandom
Non-Linear Projections Using
Rayleigh Backscattering
ty, " Redding said. " We intend to use
analog photonics, which has fundamentally
different energy scaling than
Von Neumann based digital electronics
- to perform these machine learning
tasks with lower power consumption
and with lower latency. In the current
paper, we performed an energy consumption
analysis showing the potential
for 100-1,000x lower power consumption
than a GPU depending on
the problem size. "
This research shows how optics can
be used to conduct valuable computing
tasks using passive random projections,
in this case non-linear random convolutions.
This is counter to how most
machine learning works, which typically
requires extensive training to set the
weights of a neural network.
" Instead, we show that random
weights can still perform useful comNon-Linear
PCA
PCA
Non-Linear SVM
SVM
Extreme Learning Machine
U.S. Naval Research Laboratory, Sandia National Laboratories, and University of Central Florida deliver novel contribution in fiber optics computing bringing the
Navy one step closer to faster, more efficient computing technologies for data processing, telecommunications, and artificial intelligence. Data, seen in blue, is
injected into an optical fiber as a train of pulses. The optical fiber applies a series of random convolutions, seen in yellow, projecting the data into a higher-dimensional
space to facilitate a variety of computing tasks including principal component analysis (PCA), support vector machines (SVM), or extreme learning
machines. (Image: U.S. Naval Research Laboratory)
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