Survey
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 2
341-347

Review of deep learning-based multi-core cache prefetching research

Zhang Jianxun
Qiao Xinyu
Lin Binghui
School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin 300222, China
DOI: 10.19734/j.issn.1001-3695.2023.05.0231

Abstract

The current research on the application of artificial intelligence techniques to the field of system architecture is promising, especially the research on applying deep learning to data prefetching in multicore architectures has become a research hotspot at home and abroad. This work studied the cache prefetching task based on deep learning and defined the deep learning cache prefetch model formally. Based on the introduction of current common multi-core cache architectures and prefetching techniques, this paper comprehensively analyzed the design ideas of existing typical cache prefetchers based on deep learning. The application of deep learning neural network in the field of multicore cache prefetching mainly adopts machine learning methods such as deep neural network, recurrent neural network, long and short-term memory network and attention mechanism. A comprehensive comparative analysis of existing deep learning-based data prefetching hierarchical neural models reveals that deep learning-based multicore cache prefetching techniques still have certain computational cost, model optimization, and practicality. In the future, there is still much room for research exploration and development prospect in adaptive prefetching models and the practicality of neural network prefetching models.

Foundation Support

中国高校产学研自然基金资助项目(2021FNA04016)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.05.0231
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 2
Section: Survey
Pages: 341-347
Serial Number: 1001-3695(2024)02-003-0341-07

Publish History

[2023-10-07] Accepted Paper
[2024-02-05] Printed Article

Cite This Article

张建勋, 乔欣雨, 林炳辉. 深度学习在多核缓存预取中的应用研究综述 [J]. 计算机应用研究, 2024, 41 (2): 341-347. (Zhang Jianxun, Qiao Xinyu, Lin Binghui. Review of deep learning-based multi-core cache prefetching research [J]. Application Research of Computers, 2024, 41 (2): 341-347. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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