探索用于成像和计算的光子神经网络类型-一篇评论
Svetlana N Khonina1, Nikolay L Kazanskiy1, Roman V Skidanov1
1Samara National Research University, 443086 Samara, Russia.
Nanomaterials (Basel, Switzerland)
|April 26, 2024
概括
光子神经网络 (PNN) 使用光用于人工智能和计算,比电子系统提供更快的速度和效率. 本综述涵盖了PNN类型,挑战和人工智能和机器学习的未来潜力.
科学领域:
- 光学和光子学 在光学和光子学.
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 光子神经网络 (PNN) 利用光进行计算,为电子神经网络提供了替代方案.
- PNN提供了诸如更高的处理速度,更低的能源消耗和增强的并行性等优势.
- 它们有可能解决大规模人工智能 (AI) 系统中的可扩展性和效率问题.
研究的目的:
- 提供当前最先进的光子神经网络的全面审查.
- 讨论为成像和计算应用设计的PNN.
- 探索PNN实施的挑战和未来前景.
主要方法:
- 对各种PNN架构和应用的文献综述.
- 与电子同行相比,PNN绩效指标的分析.
- 讨论实施挑战和未来的研究方向.
主要成果:
- PNN展示了通过基于光的处理来彻底改变人工智能和计算的巨大潜力.
- 目前正在开发各种PNN类型,用于专门的成像和计算任务.
- 关键的挑战包括制造,集成和算法开发,以及有前途的未来应用.
结论:
- PNN代表了未来人工智能和计算范式的转型技术.
- 克服实施挑战将释放基于光的AI的全部潜力.
- 对PNN的持续研究有望重塑先进计算和机器学习的格局.
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