Hardware-Efficient Optical Communication Architectures for AI Data Center Networks
Keywords:
optical interconnects; AI data centers; silicon photonics; wavelength-division multiplexing; hardware efficiency; network architecture; sustainability; governanceAbstract
Artificial intelligence data centers are increasingly constrained by the electrical interconnect fabric that links accelerators, memory, storage, and switching resources. Large-scale training and inference workloads generate intense east-west traffic, synchronization-heavy communication patterns, and bursty demand that traditional electrical packet switching handles with growing difficulty in terms of bandwidth density, reach, latency, and energy. Optical communication architectures offer a promising path toward higher throughput and lower signaling loss, but they are not automatically efficient. Hardware efficiency depends on the co-design of photonic devices, packaging, switching, control planes, thermal management, and deployment governance. This paper examines hardware-efficient optical communication architectures for AI data center networks from a system-level perspective. It analyzes the requirements imposed by distributed AI workloads, reviews silicon photonic and wavelength-division multiplexing building blocks, and compares point-to-point, circuit-switched, packet-switched, and hybrid electrical-optical architectures. The discussion emphasizes structural trade-offs among bandwidth, latency, energy, cost, reliability, and sustainability. It also considers operational governance, supply-chain resilience, fairness in access to advanced computing infrastructure, and policy implications. The paper argues that hardware-efficient optical AI fabrics require not only improved transceivers and switches but also coherent control, telemetry, standardization, and lifecycle accountability. Frequency-referenced multiplexing and related low-latency bandwidth-guaranteed approaches illustrate how physical-layer innovation can reshape network-level guarantees, yet their benefits depend on holistic architecture and governance choices. The conclusion outlines a research agenda for co-designed, sustainable, and equitable optical AI infrastructure.
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