KubeEdge.AI: AI Platform for Edge Devices
This work addresses the problem of simplifying AI deployment on edge devices for developers, though it appears incremental as it extends an existing edge computing framework.
The authors tackled the challenge of developing AI systems for edge devices by proposing KubeEdge.AI, a framework built on KubeEdge that provides modules for data handling, AI runtime, decision-making, and distributed queries, aiming to reduce development burdens and enhance edge-cloud coordination.
The demand for smartness in embedded systems has been mounting up drastically in the past few years. Embedded system today must address the fundamental challenges introduced by cloud computing and artificial intelligence. KubeEdge [1] is an edge computing framework build on top of Kubernetes [2]. It provides compute resource management, deployment, runtime and operation capabilities on geo-located edge computing resources, from the cloud, which is a natural fit for embedded systems. Here we propose KubeEdge.AI, an edge AI framework on top of KubeEdge. It provides a set of key modules and interfaces: a data handling and processing engine, a concise AI runtime, a decision engine, and a distributed data query interface. KubeEdge.AI will help reduce the burdens for developing specific edge/embedded AI systems and promote edge-cloud coordination and synergy.