The challenging deployment of compute-intensive applications from domains such Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces the community of computing systems to explore new design approaches. Approximate Computing appears as an emerging solution, allowing to tune the quality of results in the design of a system in order to improve the energy efficiency and/or performance. This radical paradigm shift has attracted interest from both academia and industry, resulting in significant research on approximation techniques and methodologies at different design layers (from system down to integrated circuits). Motivated by the wide appeal of Approximate Computing over the last 10 years, we conduct a two-part survey to cover key aspects (e.g., terminology and applications) and review the state-of-the art approximation techniques from all layers of the traditional computing stack. In Part II of our survey, we classify and present the technical details of application-specific and architectural approximation techniques, which both target the design of resource-efficient processors/accelerators & systems. Moreover, we present a detailed analysis of the application spectrum of Approximate Computing and discuss open challenges and future directions.

翻译：人工智能（AI）和数字信号处理（DSP）等领域中计算密集型应用的高要求部署，迫使计算系统领域探索新的设计方法。近似计算作为一种新兴解决方案应运而生，它允许在设计系统时调节结果质量，以提高能效和/或性能。这一根本性范式转变吸引了学术界和工业界的广泛关注，催生了在不同设计层级（从系统到集成电路）关于近似技术与方法论的大量研究。受过去十年间近似计算广泛影响力的推动，我们开展了两部分综述以涵盖关键方面（如术语和应用），并评述了传统计算堆栈各层级的最新近似技术。在本综述的第二部分中，我们分类阐述了应用特化与架构近似技术的技术细节，这两类技术均以资源高效型处理器/加速器及系统的设计为目标。此外，我们还详细分析了近似计算的应用谱系，并探讨了开放挑战与未来方向。