CQGA-HEFT: Q-learning-based DAG Scheduling Algorithm Using Genetic Algorithm in Clustered Many-core Platform
Author:
Affiliation:
1. Graduate School of Science and Engineering, Saitama University
Publisher
Information Processing Society of Japan
Subject
General Computer Science
Link
https://www.jstage.jst.go.jp/article/ipsjjip/30/0/30_659/_pdf
Reference30 articles.
1. [1] Tu, Y., Lin, Y. and Wang, J.: Semi-supervised learning with generative adversarial networks on digital signal modulation classification, J. CMC, Vol.55, No.2 (2018).
2. [2] Azumi, T., Maruyama, Y. and Kato, S.: ROS-lite: ROS framework for NoC-based embedded many-core platform, Proc. IROS (2020).
3. [3] Kalray MPPA Manycore, available from <https://www.kalrayinc.com/products/mppa-technology>.
4. [4] Munir, A., Ranka, S. and Gordon-Ross, A.: High-Performance Energy-Efficient Multicore Embedded Computing, TPDS, Vol.23, No.4 (2012).
5. [5] Yano, A., Igarashi, S. and Azumi, T.: Contention-free scheduling algorithm using LET paradigm for clustered many-core processor, Proc. DS-RT (2021).
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