Approximate Computing and the Efficient Machine Learning Expedition
Approximate Computing and the Efficient Machine Learning Expedition
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DOI：
10.1145/3508352.3561105
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
J. Henkel;Hai Helen Li;A. Raghunathan;M. Tahoori;Swagath Venkataramani;Xiaoxuan Yang;Georgios Zervakis
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文献类型：
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作者：
J. Henkel;Hai Helen Li;A. Raghunathan;M. Tahoori;Swagath Venkataramani;Xiaoxuan Yang;Georgios Zervakis
近似计算（AxC）长期以来一直被认为是一种以放松精度要求为代价的高效系统实现的设计替代方案。尽管AxC在各个应用领域都有研究活动，但在过去十年中，AxC在机器学习（ML）中的应用蓬勃发展。根据ML模型的定义近似概念，以及与ML应用程序相关的计算开销增加-通过相应的近似有效地减轻-导致完美的匹配和富有成效的协同作用。AxC for AI/ML已经超越了学术原型。在这项工作中，我们启发了AxC和ML的协同性，并阐明了AxC在设计高效ML系统中的影响。为此，我们对AxC for ML进行了概述和分类，并使用两个描述性的应用场景来演示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.