Emerging Trends in Optical Neural Networks
A special issue of Photonics (ISSN 2304-6732).
Deadline for manuscript submissions: 15 June 2025 | Viewed by 198
Special Issue Editors
Interests: photonic neural networks; photonic computing
Special Issue Information
Dear Colleagues,
In this era of technological evolution, optical neural networks are experiencing significant growth, primarily driven by the growing demands of artificial intelligence and neuromorphic computing. The inherent limitations of digital electronic hardware, characterized by its central processor and separated memory, beckon a shift towards non-von Neumann computing architectures. These architectures promise massively distributed, parallel processing capabilities, paving the way for applications demanding low latency, high bandwidth, and energy efficiency. Optical neural networks leverage the speed, bandwidth, and parallel processing capabilities of light to accelerate computational tasks, particularly those inherent to deep learning and artificial intelligence applications. Applications range from high-performance computing and optimization problem-solving to deep learning acceleration and quantum processing. The advancement of this field is fueled by breakthroughs in photonic integration and semiconductor manufacturing, yet achieving a viable optical processor necessitates further developments in materials, devices, architectures, software, and packaging techniques.
This Special Issue aims to showcase the latest research, breakthroughs, and innovations in the field of optical neural networks. Theoretical, numerical, and experimental papers are accepted. Topics include, but are not limited to, the following:
- Multiwavelength neural networks;
- Coherent networks;
- Spiking neural networks;
- Free-space neural networks;
- Nonlinear activation functions;
- Hardware algorithms;
- Applications.
Dr. Xiaoxuan Ma
Dr. Hao Wang
Guest Editors
Manuscript Submission Information
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Keywords
- neural networks
- photonic networks
- optoelectronic devices
- neuromorphic photonics
- optical neural networks
- convolutional neural network
- matrix–matrix multiplication
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