近似计算新范式在深度学习加速系统中的应用及研究进展

龚宇; 王丽萍; 王佑; 刘伟强

doi:10.11999/JEIT230352

近似计算新范式在深度学习加速系统中的应用及研究进展

doi: 10.11999/JEIT230352

1.
南京航空航天大学电子信息工程学院/集成电路学院南京 211106
2.
空天集成电路与微系统工信部重点实验室南京 211106

基金项目: 国家自然科学基金(62022041)，中央高校基本科研业务费(NJ2023020)

详细信息

作者简介:
龚宇：男，副研究员，研究方向为近似计算、人工智能系统芯片设计

王丽萍：女，博士生，研究方向为近似计算、人工智能系统芯片设计

王佑：男，副研究员，研究方向为近似计算、数字芯片设计

刘伟强：男，教授，研究方向为近似计算、集成电路设计等

通讯作者:
刘伟强　liuweiqiang@nuaa.edu.cn

中图分类号: TN402; TP183
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- 被引次数: 0
出版历程
- 收稿日期: 2023-05-04
- 修回日期: 2023-08-23
- 网络出版日期: 2023-08-25
- 刊出日期: 2023-09-27

Application and Research Progress of Approximate Computing as a New Computing Paradigm in AI Acceleration Systems

1.
College of Electronic and Information Engineering/College of Integrated Circuits, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2.
Key Laboratory of Aerospace Integrated Circuits and Microsystem, Ministry of Industry and Information Technology, Nanjing 211106, China

Funds: The National Natural Science Foundation of China (62022041), The Fundamental Research Funds for the Central Universities (NJ2023020)

摘要

摘要: 深度学习已经成为当前人工智能技术中最为重要的算法之一。随着应用场景不断扩展，深度学习硬件规模越来越大，计算复杂度呈现数量级提升趋势，对加速系统提出了极高能效需求。后摩尔时代，新型计算范式逐渐取代工艺微缩成为提升能效的有效方案，近似计算以牺牲部分精度的代价换取大幅能效提升，成为最具前景的设计方法之一。该文以深度学习加速系统的不同设计层次为切入，首先介绍了深度学习网络模型的算法特征，围绕算法层的近似计算方案介绍了量化方法的研究进展；其次，围绕硬件架构和电路层调研了当前深度学习加速在图像、语音等多个方向采用的近似电路和架构方案，围绕层次化的设计方法调研了当前近似计算的系统设计方法学及EDA领域的关键问题和研究进展；最后，对该领域方向进行展望，旨在推动近似计算新范式在深度学习加速系统中的应用。
- 近似计算 /
- 计算范式 /
- 深度学习 /
- 加速系统 /
- 研究进展
Abstract: Deep learning has emerged as one of the most important algorithms in artificial intelligence. With the increasing application scenarios, the hardware scales for deep learning are becoming larger, and the computational complexity has considerably increased, leading to a high demand for energy efficiency in accelerating systems. In the post-Moore’s Law era, new computing paradigms are gradually replacing process scaling as an effective solution for improving energy efficiency. One of the most promising design paradigms is approximate computing, which sacrifices some precision to improve energy efficiency. This research focuses on different design layers of deep learning acceleration systems. First, the algorithm characteristics of deep learning network models are introduced, and the research progress on quantization methods is presented in view of the approximate computing scheme at the algorithm layer. Second, approximate circuits and architectures employed in various directions such as image and speech recognition in the circuit-architecture layer are surveyed. Furthermore, the current hierarchical design methods for approximate computing as well as critical issues and research progress in Electronic Design Automation (EDA) are investigated. Finally, the future direction of this field is anticipated to promote the application of a new paradigm of approximate computing in deep learning acceleration systems.
- Approximate computing /
- Computing paradigm /
- Deep learning /
- Acceleration system /
- Research progress

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图 1 近似计算设计流程与评估方法

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