Friday, September 5, 2025
Science
No Result
View All Result
  • Login
  • HOME
  • SCIENCE NEWS
  • CONTACT US
  • HOME
  • SCIENCE NEWS
  • CONTACT US
No Result
View All Result
Scienmag
No Result
View All Result
Home Science News Technology and Engineering

Engineers bring efficient optical neural networks into focus

August 12, 2024
in Technology and Engineering
Reading Time: 4 mins read
0
The core of the optical processor
66
SHARES
597
VIEWS
Share on FacebookShare on Twitter
ADVERTISEMENT

EPFL researchers have published a programmable framework that overcomes a key computational bottleneck of optics-based artificial intelligence systems. In a series of image classification experiments, they used scattered light from a low-power laser to perform accurate, scalable computations using a fraction of the energy of electronics.

The core of the optical processor

Credit: Alain Herzog

EPFL researchers have published a programmable framework that overcomes a key computational bottleneck of optics-based artificial intelligence systems. In a series of image classification experiments, they used scattered light from a low-power laser to perform accurate, scalable computations using a fraction of the energy of electronics.

As digital artificial intelligence systems grow in size and impact, so does the energy required to train and deploy them – not to mention the associated carbon emissions. Recent research suggests that if current AI server production continues at its current pace, their annual energy consumption could outstrip that of a small country by 2027. Deep neural networks, inspired by the architecture of the human brain, are especially power-hungry due to the millions or even billions of connections between multiple layers of neuron-like processors.

To counteract this mushrooming energy demand, researchers have doubled down on efforts to implement optical computing systems, which have existed experimentally since the 1980s. These systems rely on photons to process data, and although light can theoretically be used to perform computations much faster and more efficiently than electrons, a key challenge has hindered optical systems’ ability to surpass the electronic state-of-the art.

“In order to classify data in a neural network, each node, or ‘neuron’, must make a ‘decision’ to fire or not based on weighted input data. This decision leads to what is known as a nonlinear transformation of the data, meaning the output is not directly proportional to the input,” says Christophe Moser, head of the Laboratory of Applied Photonics Devices in EPFL’s School of Engineering.

Moser explains that while digital neural networks can easily perform nonlinear transformations with transistors, in optical systems, this step requires very powerful lasers. Moser worked with students Mustafa Yildirim, Niyazi Ulas Dinc, and Ilker Oguz, as well as Optics Laboratory head Demetri Psaltis, to develop an energy-efficient method for performing these nonlinear computations optically. Their new approach involves encoding data, like the pixels of an image, in the spatial modulation of a low-power laser beam. The beam reflects back on itself several times, leading to a nonlinear multiplication of the pixels.

“Our image classification experiments on three different datasets showed that our method is scalable, and up to 1,000 times more power-efficient than state-of-the-art deep digital networks, making it a promising platform for realizing optical neural networks,” says Psaltis.

The research, supported by a Sinergia grant from the Swiss National Science Foundation, has recently been published in Nature Photonics.

A simple structural solution

In nature, photons do not directly interact with each other the way charged electrons do. To achieve nonlinear transformations in optical systems, scientists have therefore had to ‘force’ photons to interact indirectly, for example by using a light intense enough to modify the optical properties of the glass or other material it passes through.

The scientists worked around this need for a high-power laser with an elegantly simple solution: they encoded the pixels of an image spatially on the surface of a low-power laser beam. By performing this encoding twice, via adjustment of the trajectory of the beam in the encoder, the pixels are multiplied by themselves, i.e., squared. Since squaring is a non-linear transformation, this structural modification achieves the non-linearity essential to neural network calculations, at a fraction of the energy cost. This encoding can be carried out two, three or even ten times, increasing the non-linearity of the transformation and the precision of the calculation.

“We estimate that using our system, the energy required to optically compute a multiplication is eight orders of magnitude less than that required for an electronic system,” Psaltis says.

Moser and Psaltis emphasize that the scalability of their low-energy approach is a major advantage, as the ultimate goal would be to use hybrid electronic-optical systems to mitigate the energy consumption of digital neural networks. However, further engineering research is needed to achieve such scale-up. For example, because optical systems use different hardware than electronic systems, a next step that the researchers are already working on is developing a compiler to translate digital data into code that optical systems can use.



Journal

Nature Photonics

DOI

10.1038/s41566-024-01494-z

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Nonlinear Processing with Linear Optics

Article Publication Date

31-Jul-2024

Share26Tweet17
Previous Post

Heat caused over 47,000 deaths in Europe in 2023, the second highest burden of the last decade

Next Post

NK cells expressing interleukin-21 show promising antitumor activity in glioblastoma cells

Related Posts

blank
Technology and Engineering

Transforming Waste to Energy: Emission Control Innovations

September 5, 2025
blank
Technology and Engineering

Impact of Vegetable Waste on Labeo Rohita Growth

September 5, 2025
blank
Technology and Engineering

Ultra-Compact Plasmonic Nanocavity Boosts Magnetic SHG

September 5, 2025
blank
Technology and Engineering

Enhanced Solar Water Splitting Efficiency and Stability Achieved with Transparent Mesoporous WO₃ Films

September 5, 2025
blank
Technology and Engineering

Eco-Friendly Recovery of Nutrients from Biogas Slurry

September 5, 2025
blank
Technology and Engineering

Can the Judiciary Ensure Fairness in the Age of Artificial Intelligence?

September 4, 2025
Next Post
NK cells expressing interleukin-21 show promising antitumor activity in glioblastoma cells

NK cells expressing interleukin-21 show promising antitumor activity in glioblastoma cells

  • Mothers who receive childcare support from maternal grandparents show more parental warmth, finds NTU Singapore study

    Mothers who receive childcare support from maternal grandparents show more parental warmth, finds NTU Singapore study

    27544 shares
    Share 11014 Tweet 6884
  • University of Seville Breaks 120-Year-Old Mystery, Revises a Key Einstein Concept

    958 shares
    Share 383 Tweet 240
  • Bee body mass, pathogens and local climate influence heat tolerance

    643 shares
    Share 257 Tweet 161
  • Researchers record first-ever images and data of a shark experiencing a boat strike

    510 shares
    Share 204 Tweet 128
  • Warm seawater speeding up melting of ‘Doomsday Glacier,’ scientists warn

    313 shares
    Share 125 Tweet 78
Science

Embark on a thrilling journey of discovery with Scienmag.com—your ultimate source for cutting-edge breakthroughs. Immerse yourself in a world where curiosity knows no limits and tomorrow’s possibilities become today’s reality!

RECENT NEWS

  • FDX1 Drives Malignant Progression in Triple-Negative Breast Cancer
  • Transforming Waste to Energy: Emission Control Innovations
  • Thyroid Sensitivity and Bone Health in Diabetes
  • Plant Polyphenols: Key Players in Human Health

Categories

  • Agriculture
  • Anthropology
  • Archaeology
  • Athmospheric
  • Biology
  • Blog
  • Bussines
  • Cancer
  • Chemistry
  • Climate
  • Earth Science
  • Marine
  • Mathematics
  • Medicine
  • Pediatry
  • Policy
  • Psychology & Psychiatry
  • Science Education
  • Social Science
  • Space
  • Technology and Engineering

Subscribe to Blog via Email

Enter your email address to subscribe to this blog and receive notifications of new posts by email.

Join 5,183 other subscribers

© 2025 Scienmag - Science Magazine

Welcome Back!

Login to your account below

Forgotten Password?

Retrieve your password

Please enter your username or email address to reset your password.

Log In
No Result
View All Result
  • HOME
  • SCIENCE NEWS
  • CONTACT US

© 2025 Scienmag - Science Magazine

Discover more from Science

Subscribe now to keep reading and get access to the full archive.

Continue reading