Suppr超能文献

用于成像和计算的光子神经网络类型探索——综述

Exploring Types of Photonic Neural Networks for Imaging and Computing-A Review.

作者信息

Khonina Svetlana N, Kazanskiy Nikolay L, Skidanov Roman V, Butt Muhammad A

机构信息

Samara National Research University, 443086 Samara, Russia.

出版信息

Nanomaterials (Basel). 2024 Apr 17;14(8):697. doi: 10.3390/nano14080697.

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.

摘要

光子神经网络(PNNs)利用基于光的技术,在人工智能(AI)和计算领域展现出巨大潜力。与传统电子神经网络相比,它们具有更快的处理速度、更低的能源消耗以及更高的并行性。利用光的特性进行信息处理可能会彻底改变各种应用,包括复杂计算和先进的机器学习(ML)。此外,这些网络可以应对大规模人工智能系统中的可扩展性和效率挑战,有可能重塑计算和人工智能研究的未来。在这篇全面的综述中,我们提供了针对成像和计算目的而设计的各种类型PNNs的当前前沿见解。此外,我们深入探讨了它们在实施过程中遇到的复杂挑战,同时也阐明了它们为该领域带来的有前景的观点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91d8/11054149/27a84ad902cb/nanomaterials-14-00697-g003.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验