可编程超表面助力光子人工神经网络实现高效可扩展的未来智能计算
Programmable metasurfaces for future photonic artificial intelligence
- 利用可重构超表面实现光子神经网络的现场可编程能力
- 解决光子人工智能硬件规模化难题，提升能效与吞吐量优势
- 适合追求低功耗、高并发计算的下一代光子芯片研发者
光子神经网络（PNNs）凭借其固有的高并行性与低功耗特性，在能效、延迟和吞吐量方面可能挑战传统数字神经网络。然而，实现可扩展的光子人工智能解决方案仍面临挑战。若要使基于PNN的大规模光学AI模型具备商业可行性，光学计算的优势必须超过输入输出开销成本。本文探讨了现场可编程超表面技术如何成为实现可扩展光子人工智能加速器的关键硬件组件，并有望与当前数字电子技术竞争。可编程性或可重构性是PNN硬件的核心要素，支持现场训练及需微调或迁移学习的非平稳应用场景。与电子学的共集成、三维堆叠以及超表面的大规模制造将显著提升PNN的可扩展性与功能。可编程超表面有望解决当前PNN面临的一些关键挑战，推动下一代光子人工智能技术的发展。
原文摘要 · Abstract (English)
Photonic neural networks (PNNs), which share the inherent benefits of photonic systems, such as high parallelism and low power consumption, could challenge traditional digital neural networks in terms of energy efficiency, latency, and throughput. However, producing scalable photonic artificial intelligence (AI) solutions remains challenging. To make photonic AI models viable, the scalability problem needs to be solved. Large optical AI models implemented on PNNs are only commercially feasible if the advantages of optical computation outweigh the cost of their input-output overhead. In this Perspective, we discuss how field-programmable metasurface technology may become a key hardware ingredient in achieving scalable photonic AI accelerators and how it can compete with current digital electronic technologies. Programmability or reconfigurability is a pivotal component for PNN hardware, enabling in situ training and accommodating non-stationary use cases that require fine-tuning or transfer learning. Co-integration with electronics, 3D stacking, and large-scale manufacturing of metasurfaces would significantly improve PNN scalability and functionalities. Programmable metasurfaces could address some of the current challenges that PNNs face and enable next-generation photonic AI technology.
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