Earticle

초록

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.

목차

Abstract
I. INTRODUCTION
II. PRELIMINARY OBSERVATIONS
III. OPTIMIZING DFS AND SRAM ALLOCATION
IV. CONCLUSION AND FUTURE WORKS
ACKNOWLEDGMENT
REFERENCES

저자

• Changhun Han [ Department of AI Convergence Network Ajou University ]
• Seokho Yoon [ Department of Software and Computer Engineering Ajou University ]
• Sangeun Oh [ Department of AI Convergence Network Ajou University ] Corresponding Author

참고문헌