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- Title
Exploring Types of Photonic Neural Networks for Imaging and Computing—A Review.
- Authors
Khonina, Svetlana N.; Kazanskiy, Nikolay L.; Skidanov, Roman V.; Butt, Muhammad A.
- Abstract
Photonic neural networks (PNNs), utilizing light-based technologies, show immense potential in artificial intelligence (AI) and computing. Compared to traditional electronic neural networks, they offer faster processing speeds, lower energy usage, and improved parallelism. Leveraging light's properties for information processing could revolutionize diverse applications, including complex calculations and advanced machine learning (ML). Furthermore, these networks could address scalability and efficiency challenges in large-scale AI systems, potentially reshaping the future of computing and AI research. In this comprehensive review, we provide current, cutting-edge insights into diverse types of PNNs crafted for both imaging and computing purposes. Additionally, we delve into the intricate challenges they encounter during implementation, while also illuminating the promising perspectives they introduce to the field.
- Subjects
ARTIFICIAL intelligence; MACHINE learning; FEEDFORWARD neural networks; ENERGY consumption; RECURRENT neural networks
- Publication
Nanomaterials (2079-4991), 2024, Vol 14, Issue 8, p697
- ISSN
2079-4991
- Publication type
Article
- DOI
10.3390/nano14080697