Problem
Research questions and friction points this paper is trying to address.
thermal tuning
wafer-scale optical interconnects
large language models
mixture-of-experts
communication-intensive
Thermal Tuning Overhead in Wafer-Scale Optical Interconnects for LLM MoE Training: A Cross-Layer Analysis and Ferroelectric-Based Mitigation
๐ค AI Summary
ๆฌๆ้ๅฏนๅคงๅ่ฏญ่จๆจกๅ่ฎญ็ปไธญๅ
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่ฐ่ฐๆนๆณ๏ผๅๅฐไบ้ไฟก้ถๆฎต็ๅปถ่ฟ๏ผๆๅไบ็ณป็ปๆง่ฝใ
๐ Abstract
The rapid scaling of large language models (LLMs), particularly mixture-of-experts (MoE) architectures, has intensified interconnect demands because expert-parallel execution is communication-intensive. Wafer-scale optical interconnects based on dense wavelength-division multiplexing (DWDM) offer a promising path to higher bandwidth; however, conventional microring-resonator (MRR)-based links rely on thermo-optic tuning and are therefore vulnerable to workload-induced thermal fluctuations. In this work, we present a cross-layer analysis of wafer-scale optical interconnects for MoE workloads that combines workload profiling, packet-level network simulation, and transient thermal analysis. We implement a wafer-scale topology in the ht-sim simulator and construct an Ansys thermal model of a 3D-integrated GPU/EIC/PIC stack. Our results show that transient temperature variations can exceed the tracking capability of conventional thermo-optic control loops and thereby introduce repeated tuning stalls during communication phases. The stall durations injected into the network simulation are derived directly from the thermal model rather than assumed. We further evaluate a ferroelectric-based electro-optic tuning mechanism that removes the continuous thermal-tuning requirement. In a four-layer proxy simulation across three MoE models, eliminating the tuning stalls yields speedups of 2.7x for Mixtral 8x7B, 3.8x for Qwen-MoE 14.3B, and 3.3x for LLaMA-MoE 6.7B relative to the thermo-optic case. These results indicate that minimizing photonic tuning latency is important for realizing the performance potential of optical interconnects in large-scale AI systems.
Innovation
Methods, ideas, or system contributions that make the work stand out.
ferroelectric-based electro-optic tuning
thermal-tuning overhead reduction
wafer-scale optical interconnects
cross-layer analysis
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