Harnessing Photonics for Machine Intelligence

• URL: http://arxiv.org/abs/2604.10841v1
• Date: Sun, 12 Apr 2026 22:23:14 GMT
• Title: Harnessing Photonics for Machine Intelligence
• Abstract summary: Integrated photonics is emerging as a candidate for artificial intelligence (AI) acceleration.<n>This review reframes photonic computing from a circuits-and-systems perspective.<n>A central theme is cross-layer co-design and workload-adaptive programmability.
• Score: 10.097591681296873
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 evolving application domains at scale. We further argue that Electronic-Photonic Design Automation (EPDA) will be pivotal, enabling closed-loop co-optimization across simulation, inverse design, system modeling, and physical implementation. By charting a roadmap from laboratory prototypes to scalable, reproducible electronic-photonic ecosystems, this review aims to guide the CAS community toward an automated, system-centric era of photonic machine intelligence.

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