earticle

영어

Artificial Intelligence (AI) has demonstrated unprecedented performance across a multitude of sectors, including disaster response. However, deploying AI in safetycritical environments poses unique challenges, especially regarding thermal management and real-time decision-making. Utilizing Edge TPU, one of the Neural Processing Units (NPUs), has shown promise in overcoming some of these challenges. Despite its advantages, Edge TPU still has limitations in thermal management and real-time task scheduling. This study introduces an approach employing Dynamic Frequency Scaling (DFS) and SRAM allocation techniques to address these challenges. By dynamically adjusting operating frequencies and resource allocations, the proposed approach aims to optimize both thermal management and real-time performance, thereby enhancing the reliability and efficiency of AI technologies in critical applications like disaster response.