Approximate Computing for ML: State-of-the-art, Challenges and Visions
Approximate Computing for ML: State-of-the-art, Challenges and Visions
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机器学习近似计算：最先进的技术、挑战和愿景
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
J. Henkel
中科院分区：
文献类型：
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作者：
Georgios Zervakis;Hassaan Saadat;H. Amrouch;A. Gerstlauer;S. Parameswaran;J. Henkel
在本文中，我们提出了我们最先进的近似技术，涵盖了近似计算研究的主要支柱。我们的分析考虑了静态和可重构近似技术以及特定于操作的近似组件（例如，乘数）和广义近似高级合成方法。作为我们的应用目标，我们讨论了这些技术给机器学习和神经网络带来的改进。除了常规分析的性能和能量增益，我们还评估了近似计算在工作温度下带来的改进。
In this paper, we present our state-of-the-art approximate techniques that cover the main pillars of approximate computing research. Our analysis considers both static and reconfigurable approximation techniques as well as operation-specific approximate components (e.g., multipliers) and generalized approximate high-level synthesis approaches. As our application target, we discuss the improvements that such techniques bring on machine learning and neural networks. In addition to the conventionally analyzed performance and energy gains, we also evaluate the improvements that approximate computing brings in the operating temperature.
DOI：
10.1145/2966986.2967005
发表时间：
2016-11
期刊：
2016 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)
影响因子：
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作者：
Semeen Rehman;Walaa El-Harouni;M. Shafique;Akash Kumar;J. Henkel
通讯作者：
Semeen Rehman;Walaa El-Harouni;M. Shafique;Akash Kumar;J. Henkel