Trending...
- Skool Alternatives Reddit: Skool vs Circle vs Whop - Did you join one yet?
- AI Visibility: The Key to Beating Google's AI Overviews and Regaining Traffic
- New Mobile Car Detailing Platform Connects Drivers with On-Demand Local Pros
LOS ANGELES - Californer -- Diffractive deep neural network is an optical machine learning framework that blends deep learning with optical diffraction and light-matter interaction to engineer diffractive surfaces that collectively perform optical computation at the speed of light. A diffractive neural network is first designed in a computer using deep learning techniques, followed by the physical fabrication of the designed layers of the neural network using e.g., 3D printing or lithography.
Developed by researchers at UCLA [1-3], diffractive optical networks provide a low power, low latency and highly-scalable machine learning platform. In these earlier demonstrations, diffractive network models were developed to process information through a single wavelength. Addressing this limitation, UCLA researchers have designed diffractive networks that can process information using a continuum of wavelengths, expanding this all-optical computation framework into broadband optical signals [4]. Published in Light: Science & Applications, UCLA researchers demonstrated the success of this new framework by creating a series of optical components that filter broadband input light into desired sub-bands. These deep learning-based diffractive systems also control the precise location of each filtered band of radiation at the output plane, demonstrating spatially-controlled wavelength de-multiplexing in terahertz (THz) part of the electromagnetic spectrum. After their design in a computer, these broadband diffractive networks were fabricated with a 3D-printer and successfully tested using a pulsed THz source emitting a continuum of wavelengths between 60 and 3,000 micrometers.
More on The Californer
This research was led by Dr. Aydogan Ozcan, UCLA Chancellor's Professor of electrical and computer engineering (ECE). The other authors of this work are graduate students Yi Luo, Deniz Mengu, Muhammed Veli, post-doctoral researcher Dr. Nezih T. Yardimci, Adjunct Professor Dr. Yair Rivenson, as well as Professor Mona Jarrahi, all with the ECE department at UCLA.
This new method is also broadly applicable to different parts of the electromagnetic spectrum, including the visible band, and thus, represents a critical milestone for diffractive optical networks toward their widespread utilization in modern day optical components and machine learning systems, covering a wide range of applications in for example robotics, autonomous vehicles and surveillance.
Link to the paper: https://www.nature.com/articles/s41377-019-0223-1
References:
1. Lin X, et al. All-optical machine learning using diffractive deep neural networks. Science 2018; 361: 1004.
More on The Californer
2. Li J, et al. Class-specific differential detection in diffractive optical neural networks improves inference accuracy. Adv Photon 2019; 1: 1.
3. Mengu D, et al. Analysis of Diffractive Optical Neural Networks and Their Integration With Electronic Neural Networks. IEEE Journal of Selected Topics in Quantum Electronics 2020; 26: 1–14.
4. Y. Luo, et al. "Design of task-specific optical systems using broadband diffractive neural networks," Light: Science & Applications, DOI: 10.1038/s41377-019-0223-1 (2019)
Developed by researchers at UCLA [1-3], diffractive optical networks provide a low power, low latency and highly-scalable machine learning platform. In these earlier demonstrations, diffractive network models were developed to process information through a single wavelength. Addressing this limitation, UCLA researchers have designed diffractive networks that can process information using a continuum of wavelengths, expanding this all-optical computation framework into broadband optical signals [4]. Published in Light: Science & Applications, UCLA researchers demonstrated the success of this new framework by creating a series of optical components that filter broadband input light into desired sub-bands. These deep learning-based diffractive systems also control the precise location of each filtered band of radiation at the output plane, demonstrating spatially-controlled wavelength de-multiplexing in terahertz (THz) part of the electromagnetic spectrum. After their design in a computer, these broadband diffractive networks were fabricated with a 3D-printer and successfully tested using a pulsed THz source emitting a continuum of wavelengths between 60 and 3,000 micrometers.
More on The Californer
- California: Governor Newsom statement on the court temporarily blocking the Trump Administration's unlawful immigration tactics in the Los Angeles area
- Governor Newsom urges Californians to take precautions as state endures triple digit heat, smoky conditions
- Yvette Kendall Secures $6 Million Deal with The Sessions Studios for Horror Thriller, "NORTH"
- Buy The Crave Launches Premium Creatine and Natural Wellness Supplements for Modern Lifestyles
- Long Beach Parks, Recreation and Marine's Homeland Cultural Center Presents DanceFest at Cesar Chavez Park Amphitheater on August 16
This research was led by Dr. Aydogan Ozcan, UCLA Chancellor's Professor of electrical and computer engineering (ECE). The other authors of this work are graduate students Yi Luo, Deniz Mengu, Muhammed Veli, post-doctoral researcher Dr. Nezih T. Yardimci, Adjunct Professor Dr. Yair Rivenson, as well as Professor Mona Jarrahi, all with the ECE department at UCLA.
This new method is also broadly applicable to different parts of the electromagnetic spectrum, including the visible band, and thus, represents a critical milestone for diffractive optical networks toward their widespread utilization in modern day optical components and machine learning systems, covering a wide range of applications in for example robotics, autonomous vehicles and surveillance.
Link to the paper: https://www.nature.com/articles/s41377-019-0223-1
References:
1. Lin X, et al. All-optical machine learning using diffractive deep neural networks. Science 2018; 361: 1004.
More on The Californer
- Sisu, a Portrait of Grit, Connection and Triumph, Premieres on Documentary Showcase
- New Liz Taylor Book Coming Soon: Chasing Elizabeth Taylor
- City of Long Beach Experienced a 4% Decrease in Fireworks-Related Reports on July 4
- The Blue Luna Encourages Local Schools to Take Steps to Enhance Safety for Students and Staff
- Wise Business Plans Launches Turnkey Startup Packages to Help Entrepreneurs Start and Scale
2. Li J, et al. Class-specific differential detection in diffractive optical neural networks improves inference accuracy. Adv Photon 2019; 1: 1.
3. Mengu D, et al. Analysis of Diffractive Optical Neural Networks and Their Integration With Electronic Neural Networks. IEEE Journal of Selected Topics in Quantum Electronics 2020; 26: 1–14.
4. Y. Luo, et al. "Design of task-specific optical systems using broadband diffractive neural networks," Light: Science & Applications, DOI: 10.1038/s41377-019-0223-1 (2019)
Source: UCLA ITA
Filed Under: Science
0 Comments
Latest on The Californer
- Easton & Easton, LLP Files Suit Against The Dwelling Place Anaheim & Vineyard USA Over Abuse Allegations
- AI Visibility: The Key to Beating Google's AI Overviews and Regaining Traffic
- First Partner highlights apprenticeship program helping underrepresented youth break into careers in California's iconic entertainment industry
- Stuck Doing Math or Figuring Out Life's Numbers? Calculator.now Makes It Stupidly Simple
- Cal State LA secures funding for two artificial intelligence projects from CSU
- Colbert Packaging Announces WBENC Recognition
- New Mobile Car Detailing Platform Connects Drivers with On-Demand Local Pros
- Over the past three months, California seized $476 million worth of unlicensed cannabis products
- California scores more clean energy records: 9 in 10 days this year partially powered by 100% clean energy
- "Mobile Suit Gundam" Takes Over San Diego Comic-Con 2025
- DivX Empowers Media Enthusiasts with Free Expert Guides for Advanced MP4 Management
- Assent Expands Executive Team to Accelerate Global Growth & Innovation
- The World's Largest Green Economic Revolution Emerges as Nature, Tech, and Finance Converge
- Hamilton Zanze Sponsors the Acquisition of Two Garden-Style Communities in Reno Area
- Meet a Scientologist Captures Greece's Timeless Beauty with Videographer Lambros Malamas
- Vinnetwork Unveils Decentralized AI Platform with Vinnetwork(VIN) Token to Challenge Tech Giants' Data Monopoly
- Moovs Launches Advanced Contact Center Solution for Large-Scale Transportation Operations
- Centennial Flyers to Become Colorado's First Launch Customer for All-Electric B23 Energic Aircraft
- Second Annual Artists' Rights Advocate Award to Be Presented at The Comedy Store on July 17th
- Pyro Marketing Opens New Digital Marketing Company to Power Growth for Fitness and Ecommerce Brands