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

GONG Yu; WANG Liping; WANG You; LIU Weiqiang

doi:10.11999/JEIT230352

Volume 45 Issue 9

Sep. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(9): 3098-3108

GONG Yu, WANG Liping, WANG You, LIU Weiqiang. Application and Research Progress of Approximate Computing as a New Computing Paradigm in AI Acceleration Systems[J]. Journal of Electronics & Information Technology, 2023, 45(9): 3098-3108. doi: 10.11999/JEIT230352

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GONG Yu, WANG Liping, WANG You, LIU Weiqiang. Application and Research Progress of Approximate Computing as a New Computing Paradigm in AI Acceleration Systems[J]. Journal of Electronics & Information Technology, 2023, 45(9): 3098-3108. doi: 10.11999/JEIT230352

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

doi: 10.11999/JEIT230352

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)

Received Date: 2023-05-04
Rev Recd Date: 2023-08-23

Available Online: 2023-08-25

Publish Date: 2023-09-27

Abstract

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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References(92)

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