Readle：一个基于人工智能的边缘系统设计的形式化框架
Readle: A Formal Framework for Designing AI-based Edge Systems
摘要：
随着人工智能驱动的系统在边缘（也称为边缘智能系统）中的广泛使用，例如自动驾驶汽车，可穿戴生物技术设备，智能制造等，这种系统对我们的日常生活变得非常重要。设计边缘智能系统的一个挑战是，我们必须处理构成此类系统基础的两个设计空间中的大量约束：边缘设计空间和深度学习设计空间。因此，在这项工作中，提出了一种新的系统的，可扩展的，手动方法，READLE，用于创建边缘智能系统中的规范表示，以连贯的方式捕获边缘系统设计空间中的约束（例如，时序约束和其他性能约束）和深度学习空间中的约束（例如，模型训练持续时间，所需的准确度）。特别是，READLE利用实时逻辑和二元决策图的优势来生成统一的规范。还讨论了在构建READLE过程中学到的一些见解，这将有助于边缘智能系统正式规范领域的未来研究。
英文摘要：
With the wide spread use of AI-driven systems in the edge (a.k.a edge intelligence systems), such as autonomous driving vehicles, wearable biotech devices, intelligent manufacturing, etc., such systems are becoming very critical for our day-to-day lives. A challenge in designing edge intelligence systems is that we have to deal with a large number of constraints in two design spaces that form the basis of such systems: the edge design space and the deep learning design space. Thus in this work, a new systematic, extendable, manual approach, READLE, is proposed for creating representations of specifications in edge intelligent systems, capturing constraints in the edge system design space (e.g. timing constraints and other performance constraints) and constraints in the deep learning space (e.g. model training duration, required level of accuracy) in a coherent fashion. In particular, READLE leverages benefits of real-time logic and binary decision diagrams to generate unified specifications. Several insights learned in building READLE are also discussed, which should help in future research in the domain of formal specifications for edge intelligent systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
