A Decentralised, Autonomous, and Congestion-aware Thermal Monitoring Infrastructure for Photonic Network-on-Chip

Optical network-on-chips (oNoCs) are a promising candidate to replace electrical NoCs in many-core systems. Nanophotonic components provide high bandwidth and short transmission delay but they are very sensitive to the temperature. Temperature gradients are typically found on many-core systems. The precise reconstruction of the core's temperature and the prediction of its evolution are an inevitable requirement for an effective thermal management in oNoCs. In this work we present the first distributed and autonomous thermal monitoring infrastructure for oNoCs. It copes with the intrinsic temperature sensor's noise, provides accurate thermal predictions, and requires low communication overhead. Our approach combines Kalman filters, linear predictors, local data and global transmission to achieve an efficient implementation. The work reports a thorough analysis at the gate-level of the area overhead incurred by the communication and computation sub-modules. It shows the practicability of the approach. Further, experimental results for a 16-core SoC demonstrate that our prediction model is able to reduce by more than 92 % the communication requirements of the monitoring infrastructure.