Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 9
2616-2620

Processing in memory deployment optimization algorithm based on deep reinforcement learning

Hu Yidi
Xia Yinshui
Faculty of Electrical Engineering & Computer Science, Ningbo University, Ningbo Zhejiang 315211, China
DOI: 10.19734/j.issn.1001-3695.2023.02.0047

Abstract

To address the issues of computational latency and high operational power consumption caused by the deployment of large-scale neural networks for in-memory computing, this paper proposed a deep reinforcement learning-based optimization algorithm for neural network deployment. Firstly, it established a task model for Markov decision processes, which optimized the latency and power consumption of the neural network and completed the deployment of the on-chip computing core. Secondly, to tackle the challenges of excessive solution space and insufficient exploration capability during the optimization process, it introduced a deployment optimization algorithm based on deep reinforcement learning to obtain a near-optimal neural network deployment strategy. Lastly, it proposed a reward strategy grounded in intrinsic motivation to address the lack of exploration abi-lity in reinforcement learning, encouraging the exploration of unknown solution spaces, enhancing the quality of deployment, and resolving issues such as getting trapped in local optimality. Experimental results demonstrate that the proposed algorithm further optimizes power consumption and latency compared to current reinforcement learning algorithms.

Foundation Support

国家自然科学基金资助项目(62131010,U22A2013)
浙江省创新群体资助项目(LDT23F4021F04)
宁波高新区重大技术创新资助项目(2022BCX050001)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.02.0047
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 9
Section: Algorithm Research & Explore
Pages: 2616-2620
Serial Number: 1001-3695(2023)09-008-2616-05

Publish History

[2023-04-26] Accepted Paper
[2023-09-05] Printed Article

Cite This Article

胡益笛, 夏银水. 基于深度强化学习的存内计算部署优化算法 [J]. 计算机应用研究, 2023, 40 (9): 2616-2620. (Hu Yidi, Xia Yinshui. Processing in memory deployment optimization algorithm based on deep reinforcement learning [J]. Application Research of Computers, 2023, 40 (9): 2616-2620. )

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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