Task-level Thermal Modeling for Temperature Management of Edge TPU

Edge TPU (Tensor Processing Unit) is being widely utilized in various edge computing applications as a high-efficiency, low-power accelerator for deep learning computations. However, temperature rise in Edge TPU can lead to performance degradation, reduced stability, and shortened lifespan, necessitating temperature management through thermal modeling. This paper proposes a task-level thermal modeling technique for predicting Edge TPU temperature. The proposed method estimates power consumption of CPU and Edge TPU based on workloads of various deep learning tasks and predicts the convergence temperature of Edge TPU using a steady temperature model. Through experiments, we confirmed that the proposed method accurately predicts Edge TPU temperature for various workloads. The average prediction error was 0.7°C. This study is expected to serve as a foundation for developing temperature management techniques by presenting an effective temperature prediction model that considers the thermal characteristics of Edge TPU.