Edge Computing
Edge computing focuses on processing data closer to its source, minimizing latency and bandwidth usage for applications like autonomous driving and IoT. Current research emphasizes efficient resource allocation strategies, often employing large language models (LLMs) and reinforcement learning algorithms to optimize task scheduling and model deployment on resource-constrained edge devices, including the use of spiking neural networks and implicit neural representations for improved efficiency. This field is significant for enabling real-time, privacy-preserving AI applications across diverse sectors, driving advancements in both hardware and software architectures for distributed computing.
Papers
TinyML Security: Exploring Vulnerabilities in Resource-Constrained Machine Learning Systems
Jacob Huckelberry, Yuke Zhang, Allison Sansone, James Mickens, Peter A. Beerel, Vijay Janapa Reddi
Real-time Monitoring and Analysis of Track and Field Athletes Based on Edge Computing and Deep Reinforcement Learning Algorithm
Xiaowei Tang, Bin Long, Li Zhou
Energy-Efficient Computation with DVFS using Deep Reinforcement Learning for Multi-Task Systems in Edge Computing
Xinyi Li, Ti Zhou, Haoyu Wang, Man Lin
EEPNet: Efficient Edge Pixel-based Matching Network for Cross-Modal Dynamic Registration between LiDAR and Camera
Yuanchao Yue, Hui Yuan, Suai Li, Qi Jiang