Optical neural network could lead to intelligent cameras

August 26, 2019

UCLA engineers have made major improvements on their design of an optical neural network -a device inspired by how the human brain works - that can identify objects or process information at the speed of light.

The development could lead to intelligent camera systems that figure out what they are seeing simply by the patterns of light that run through a 3D engineered material structure. Their new design takes advantage of the parallelization and scalability of optical-based computational systems.

For example, such systems could be incorporated into self-driving cars or robots, helping them make near-instantaneous decisions faster and using less power than computer-based systems that need additional time to identify an object after it's been seen.

The technology was first introduced by the UCLA group in 2018. The system uses a series of 3D-printed wafers or layers with uneven surfaces that transmit or reflect incoming light - they're reminiscent in look and effect to frosted glass. These layers have tens of thousands of pixel points - essentially these are artificial neurons that form an engineered volume of material that computes all-optically. Each object will have a unique light pathway through the 3D fabricated layers.

Behind those layers are several light detectors, each previously assigned in a computer to deduce what the input object is by where the most light ends up after traveling through the layers.

For example, if it's trained to figure out handwritten digits, then the detector programmed to identify a "5" will see the most of the light hit that detector after the image of a "5" has traveled through the layers.

In this recent study, published in the open access journal Advanced Photonics, the UCLA researchers have significantly increased the system's accuracy by adding a second set of detectors to the system, and therefore each object type is now represented with two detectors rather than one. The researchers aimed to increase the signal difference between a detector pair assigned to an object type. Intuitively, this is similar to weighing two stones simultaneously with the left and right hands - it is easier this way to differentiate if they are of similar weight or have different weights.

This differential detection scheme helped UCLA researchers improve their prediction accuracy for unknown objects that were seen by their optical neural network.

"Such a system performs machine-learning tasks with light-matter interaction and optical diffraction inside a 3D fabricated material structure, at the speed of light and without the need for extensive power, except the illumination light and a simple detector circuitry," said Aydogan Ozcan, Chancellor's Professor of Electrical and Computer Engineering and the principal investigator on the research. "This advance could enable task-specific smart cameras that perform computation on a scene using only photons and light-matter interaction, making it extremely fast and power efficient."

The researchers tested their system's accuracy using image datasets of hand-written digits, items of clothing, and a broader set of various vehicles and animals known as the CIFAR-10 image dataset. They found image recognition accuracy rates of 98.6%, 91.1% and 51.4% respectively.

Those results compare very favorably to earlier generations of all-electronic deep neural nets. While more recent electronic systems have better performance, the researchers suggest that all-optical systems have advantages in inference speed, low-power, and can be scaled up to accommodate and identify many more objects in parallel.
-end-
Other authors on the study include graduate students Jingxi Li, Deniz Mengu and Yi Luo; and Yair Rivenson, a UCLA assistant adjunct professor of electrical and computer engineering.

Ozcan also has UCLA faculty appointments in bioengineering and in surgery at the David Geffen School of Medicine. He is the associate director of the UCLA California NanoSystems Institute and is an HHMI professor.

The study was supported by the Koç Group, the National Science Foundation and the Howard Hughes Medical Institute.

UCLA Samueli School of Engineering

Related Science Articles from Brightsurf:

75 science societies urge the education department to base Title IX sexual harassment regulations on evidence and science
The American Educational Research Association (AERA) and the American Association for the Advancement of Science (AAAS) today led 75 scientific societies in submitting comments on the US Department of Education's proposed changes to Title IX regulations.

Science/Science Careers' survey ranks top biotech, biopharma, and pharma employers
The Science and Science Careers' 2018 annual Top Employers Survey polled employees in the biotechnology, biopharmaceutical, pharmaceutical, and related industries to determine the 20 best employers in these industries as well as their driving characteristics.

Science in the palm of your hand: How citizen science transforms passive learners
Citizen science projects can engage even children who previously were not interested in science.

Applied science may yield more translational research publications than basic science
While translational research can happen at any stage of the research process, a recent investigation of behavioral and social science research awards granted by the NIH between 2008 and 2014 revealed that applied science yielded a higher volume of translational research publications than basic science, according to a study published May 9, 2018 in the open-access journal PLOS ONE by Xueying Han from the Science and Technology Policy Institute, USA, and colleagues.

Prominent academics, including Salk's Thomas Albright, call for more science in forensic science
Six scientists who recently served on the National Commission on Forensic Science are calling on the scientific community at large to advocate for increased research and financial support of forensic science as well as the introduction of empirical testing requirements to ensure the validity of outcomes.

World Science Forum 2017 Jordan issues Science for Peace Declaration
On behalf of the coordinating organizations responsible for delivering the World Science Forum Jordan, the concluding Science for Peace Declaration issued at the Dead Sea represents a global call for action to science and society to build a future that promises greater equality, security and opportunity for all, and in which science plays an increasingly prominent role as an enabler of fair and sustainable development.

PETA science group promotes animal-free science at society of toxicology conference
The PETA International Science Consortium Ltd. is presenting two posters on animal-free methods for testing inhalation toxicity at the 56th annual Society of Toxicology (SOT) meeting March 12 to 16, 2017, in Baltimore, Maryland.

Citizen Science in the Digital Age: Rhetoric, Science and Public Engagement
James Wynn's timely investigation highlights scientific studies grounded in publicly gathered data and probes the rhetoric these studies employ.

Science/Science Careers' survey ranks top biotech, pharma, and biopharma employers
The Science and Science Careers' 2016 annual Top Employers Survey polled employees in the biotechnology, biopharmaceutical, pharmaceutical, and related industries to determine the 20 best employers in these industries as well as their driving characteristics.

Three natural science professors win TJ Park Science Fellowship
Professor Jung-Min Kee (Department of Chemistry, UNIST), Professor Kyudong Choi (Department of Mathematical Sciences, UNIST), and Professor Kwanpyo Kim (Department of Physics, UNIST) are the recipients of the Cheong-Am (TJ Park) Science Fellowship of the year 2016.

Read More: Science News and Science Current Events
Brightsurf.com is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to Amazon.com.