Programmable metasurfaces for future photonic artificial intelligence
Loubnan Abou-Hamdan, Emil Marinov, Peter Wiecha, Philipp del Hougne, Tianyu Wang, Patrice Genevet

TL;DR
This paper explores how programmable metasurfaces can serve as a scalable, reconfigurable hardware platform for photonic neural networks, potentially revolutionizing photonic AI by improving scalability, training, and integration.
Contribution
It proposes the use of field-programmable metasurfaces as a key hardware component to enable scalable, reconfigurable photonic AI accelerators, addressing current scalability challenges.
Findings
Programmable metasurfaces can enable in situ training of photonic neural networks.
Integration with electronics and 3D stacking can enhance scalability.
Programmable metasurfaces may outperform current digital electronic technologies in photonic AI applications.
Abstract
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,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
