THz-SynC: Collective Synthesis with Contextual-Bandit-Assisted Coordination for Reconfigurable Hybrid Optical-THz AI Datacenters

Abstract

Terahertz (THz) wireless interconnects offer high-capacity, low-latency, and energy-efficient rack-to-rack links capable of on-demand connectivity reconfiguration, serving as a promising complement to optical fabrics for communication-intensive distributed AI training datacenters. However, co-optimizing optical and THz resources to minimize collective completion time and transmission energy remains challenging due to dynamic optical congestion, THz channel fluctuations, and heterogeneous compute stragglers. Existing reconfigurable data-center designs predominantly optimize network topology and traffic routing, with limited consideration of collective communication semantics in distributed AI workloads over hybrid fabrics. To address these challenges, we propose THz-SynC, a novel framework that integrates collective synthesis with contextual-bandit-assisted hybrid-fabric coordination to optimize the tradeoff between collective completion time and transmission energy. By exploiting collective-specific semantics, THz-SynC synthesizes tailored communication topologies for All-to-All and AllReduce patterns while dynamically allocating THz resources. Furthermore, a contextual-bandit coordinator adaptively routes communication chunks across optical and THz links and selects rack power budgets leveraging real-time observations of network states and collective semantics. Trace-driven evaluations show that THz-SynC outperforms wired-only, wireless-only, and hybrid baselines, achieving a superior delay-energy Pareto frontier under dynamic network conditions.