论文标题
近似计算和高效的机器学习探险
Approximate Computing and the Efficient Machine Learning Expedition
论文作者
论文摘要
近似计算（AXC）长期以来一直被接受为以放松精度要求为代价的有效系统实施的设计替代方法。尽管在各种应用领域进行了AXC研究活动，但AXC在过去的十年中蓬勃发展，当时它将其应用于机器学习（ML）。通过定义，ML模型的近似概念，但同时增加了与ML应用相关的计算开销 - 通过将相应的近似值指向完美匹配和富有成果的协同作用，可以有效地减轻。 AI/ML的AXC超越了学术原型。在这项工作中，我们启发了AXC和ML的协同性质，并阐明了AXC在设计有效的ML系统中的影响。为此，我们提出了ML的AXC的概述和分类法，并使用两个描述性应用程序场景来演示AXC如何提高ML系统的效率。
Approximate computing (AxC) has been long accepted as a design alternative for efficient system implementation at the cost of relaxed accuracy requirements. Despite the AxC research activities in various application domains, AxC thrived the past decade when it was applied in Machine Learning (ML). The by definition approximate notion of ML models but also the increased computational overheads associated with ML applications-that were effectively mitigated by corresponding approximations-led to a perfect matching and a fruitful synergy. AxC for AI/ML has transcended beyond academic prototypes. In this work, we enlighten the synergistic nature of AxC and ML and elucidate the impact of AxC in designing efficient ML systems. To that end, we present an overview and taxonomy of AxC for ML and use two descriptive application scenarios to demonstrate how AxC boosts the efficiency of ML systems.
