The union of Edge Computing (EC) and Artificial Intelligence (AI) has brought forward the Edge AI concept to provide intelligent solutions close to the end-user environment, for privacy preservation, low latency to real-time performance, and resource optimization. Machine Learning (ML), as the most advanced branch of AI in the past few years, has shown encouraging results and applications in the edge environment. Nevertheless, edge-powered ML solutions are more complex to realize due to the joint constraints from both edge computing and AI domains, and the corresponding solutions are expected to be efficient and adapted in technologies such as data processing, model compression, distributed inference, and advanced learning paradigms for Edge ML requirements. Despite the fact that a great deal of the attention garnered by Edge ML is gained in both the academic and industrial communities, we noticed the lack of a complete survey on existing Edge ML technologies to provide a common understanding of this concept. To tackle this, this paper aims at providing a comprehensive taxonomy and a systematic review of Edge ML techniques, focusing on the soft computing aspects of existing paradigms and techniques. We start by identifying the Edge ML requirements driven by the joint constraints. We then extensively survey more than twenty paradigms and techniques along with their representative work, covering two main parts: edge inference, and edge learning. In particular, we analyze how each technique fits into Edge ML by meeting a subset of the identified requirements. We also summarize Edge ML frameworks and open issues to shed light on future directions for Edge ML.

翻译：边缘计算与人工智能的结合催生了边缘AI概念，旨在提供接近终端环境的智能解决方案，实现隐私保护、实时低延迟性能及资源优化。作为近年来人工智能领域最先进的分支，机器学习在边缘环境中展现了令人鼓舞的成果与应用。然而，由于边缘计算与AI领域的联合约束，边缘赋能的机器学习解决方案实现更为复杂，相应方案需在数据处理、模型压缩、分布式推理以及面向边缘ML需求的先进学习范式等技术层面实现高效适配。尽管边缘ML在学术界和工业界均获得广泛关注，但我们注意到尚缺乏对现有边缘ML技术的全面综述以形成对该概念的共识认知。为此，本文旨在提出边缘ML技术的系统性分类与综述，聚焦现有范式与技术的软计算层面。我们首先识别由联合约束驱动的边缘ML需求，继而广泛调研二十余种范式与技术及其代表性工作，涵盖边缘推理与边缘学习两大核心领域。特别地，我们通过分析每项技术如何满足特定需求子集来阐明其与边缘ML的适配性，并总结边缘ML框架与开放性问题，以指明边缘ML的未来发展方向。