The rapid growth of demanding applications in domains applying multimedia processing and machine learning has marked a new era for edge and cloud computing. These applications involve massive data and compute-intensive tasks, and thus, typical computing paradigms in embedded systems and data centers are stressed to meet the worldwide demand for high performance. Concurrently, the landscape of the semiconductor field in the last 15 years has constituted power as a first-class design concern. As a result, the community of computing systems is forced to find alternative design approaches to facilitate high-performance and/or power-efficient computing. Among the examined solutions, Approximate Computing has attracted an ever-increasing interest, with research works applying approximations across the entire traditional computing stack, i.e., at software, hardware, and architectural levels. Over the last decade, there is a plethora of approximation techniques in software (programs, frameworks, compilers, runtimes, languages), hardware (circuits, accelerators), and architectures (processors, memories). The current article is Part I of our comprehensive survey on Approximate Computing, and it reviews its motivation, terminology and principles, as well it classifies and presents the technical details of the state-of-the-art software and hardware approximation techniques.

翻译：多媒体处理和机器学习等领域的应用需求快速增长，标志着边缘计算和云计算进入新时代。这些应用涉及海量数据和计算密集型任务，因此嵌入式系统和数据中心中的典型计算范式面临压力，需满足全球对高性能计算的需求。与此同时，过去15年半导体领域的格局已将功耗列为设计的首要关注点。因此，计算系统领域被迫寻找替代设计方法以实现高性能和/或节能计算。在已研究的解决方案中，近似计算吸引了日益增长的兴趣，相关研究将近似技术应用于整个传统计算栈，即软件、硬件和架构层面。过去十年间，软件（程序、框架、编译器、运行时、语言）、硬件（电路、加速器）和架构（处理器、存储器）中涌现了大量近似技术。本文是近似计算综合综述的第一部分，回顾了其动机、术语和原则，同时对现有最先进的软硬件近似技术进行了分类并阐述了技术细节。