Khonina Svetlana N, Kazanskiy Nikolay L, Skidanov Roman V, Butt Muhammad A
Samara National Research University, 443086 Samara, Russia.
Nanomaterials (Basel). 2024 Apr 17;14(8):697. doi: 10.3390/nano14080697.
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.
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。