Invited Paper: Towards the Efficiency, Heterogeneity, and Robustness of Edge AI
Over the past decade, there has been a persistent trend in edge computing, driving the migration of intelligence closer to the edge. The increasing need to process data locally has fueled the deployment of highly efficient computing hardware and artificial intelligence (AI) models onto edge devices. The performance and robustness of edge computing systems are significantly influenced by the heterogeneity of computing systems and the diverse nature of data to be processed by each edge device. This paper aims to explore the principles of software/hardware co-design for edge computing systems in AI applications. We will delve into the robustness concerns faced by edge AI due to the inherent heterogeneity of systems and data. Furthermore, we will present various solutions that effectively mitigate these adverse effects and enhance the resilience of edge AI systems.
