Harnessing Photonics for Machine Intelligence

2026-04-12

AI Summary

This work addresses the computational bottlenecks in power, memory, and interconnect that hinder machine intelligence in the post-Moore era by proposing a bottleneck-driven photonic computing taxonomy that clarifies its advantageous application scenarios. Leveraging integrated photonics, cross-layer co-design, inverse design, and system-level modeling, the authors develop an electronic-photonic co-design automation (EPDA) methodology to realize a programmable, full-stack system adaptive to diverse workloads. This research provides a systematic roadmap toward a scalable and reproducible optoelectronic intelligent computing ecosystem, advancing photonic machine intelligence into a new phase of automation and practical deployment.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Learning & Optimization for CV
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• machine intelligence
• post-Moore era
• photonic computing
• compute substrates
• system scalability

Innovation

• photonic computing
• cross-layer co-design
• Electronic-Photonic Design Automation (EPDA)
• workload-adaptive programmability
• bottleneck-driven taxonomy