Harnessing Photonics for Machine Intelligence
Hanqing Zhu, Shupeng Ning, Hongjian Zhou, Ziang Yin, Ray T. Chen, Jiaqi Gu, David Z. Pan

TL;DR
This review explores how integrated photonics can revolutionize AI acceleration by enabling scalable, efficient, and adaptable computing systems through cross-layer design and automation.
Contribution
It introduces a comprehensive system-level perspective on photonic computing, emphasizing co-design, workload adaptability, and the role of electronic-photonic design automation.
Findings
Photonic computing offers high bandwidth and parallelism for AI workloads.
Cross-layer co-design enhances efficiency and versatility in photonic systems.
Electronic-Photonic Design Automation is crucial for scalable system development.
Abstract
The exponential growth of machine-intelligence workloads is colliding with the power, memory, and interconnect limits of the post-Moore era, motivating compute substrates that scale beyond transistor density alone. Integrated photonics is emerging as a candidate for artificial intelligence (AI) acceleration by exploiting optical bandwidth and parallelism to reshape data movement and computation. This review reframes photonic computing from a circuits-and-systems perspective, moving beyond building-block progress toward cross-layer system analysis and full-stack design automation. We synthesize recent advances through a bottleneck-driven taxonomy that delineates the operating regimes and scaling trends where photonics can deliver end-to-end sustained benefits. A central theme is cross-layer co-design and workload-adaptive programmability to sustain high efficiency and versatility across…
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