Summary of Artificial Neural Networks For Photonic Applications: From Algorithms to Implementation, by Pedro Freire et al.
Artificial Neural Networks for Photonic Applications: From Algorithms to Implementation
by Pedro Freire, Egor Manuylovich, Jaroslaw E. Prilepsky, Sergei K. Turitsy
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Signal Processing (eess.SP); Optics (physics.optics)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This tutorial-review on artificial neural network applications in photonics targets a broad audience, ranging from optical research and engineering communities to computer science and applied mathematics. The paper focuses on key properties and peculiarities of core neural network types relevant to photonics, linking theoretical design to hardware realizations. It discusses recent developments and progress for several selected applications, including optical communications, imaging, sensing, and material design. The review emphasizes evaluating complexity in the transition from algorithms to hardware implementation, using introduced characteristics to analyze optical communication applications compared to benchmark signal processing methods. Novel compression strategies are combined with well-known model techniques used in machine learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how artificial intelligence can help us in the field of light and lasers. It explains how certain types of AI models work and how they can be used in different areas like communication, imaging, sensing, and making new materials. The authors also talk about how to measure the complexity of these AI models when moving from computer algorithms to real-life devices. They compare this with other methods used in signal processing and show that their approach is more effective. |
Keywords
» Artificial intelligence » Machine learning » Neural network » Signal processing
