Kyushu University Academic Staff Educational and Research Activities Database
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Satoshi Kawakami Last modified date:2019.05.21



Undergraduate School


E-Mail
Phone
092-802-3668
Academic Degree
Dr. Eng. (Mar. 2019)
Country of degree conferring institution (Overseas)
No
Field of Specialization
Computer Architecture, Nano-Photonics
Total Priod of education and research career in the foreign country
00years02months
Research
Research Interests
  • Next-Generation Computer System Architecture
    keyword : Computer architecture, High-performance low-power computing, Nano-photonic computing
    2019.04~2019.04.
Academic Activities
Papers
1. Satoshi Kawakami, Takatsugu Ono, Toshiyuki Ohtsuka, Inoue Koji, Parallel precomputation with input value prediction for model predictive control systems, IEICE Transactions on Information and Systems, 10.1587/transinf.2018PAP0003, E101D, 12, 2864-2877, 2018.12, We propose a parallel precomputation method for real-time model predictive control. The key idea is to use predicted input values produced by model predictive control to solve an optimal control problem in advance. It is well known that control systems are not suitable for multi- or many-core processors because feedback-loop control systems are inherently based on sequential operations. However, since the proposed method does not rely on conventional thread-/data-level parallelism, it can be easily applied to such control systems without changing the algorithm in applications. A practical evaluation using three real-world model predictive control system simulation programs demonstrates drastic performance improvement without degrading control quality offered by the proposed method..
Presentations
1. Satoshi Kawakami, Akihito Iwanaga, Inoue Koji, Many-core acceleration for model predictive control systems, 1st International Workshop on Many-Core Embedded Systems, MES 2013, in Conjunction with the 40th Annual IEEE/ACM International Symposium on Computer Architecture, ISCA 2013, 2013.06, This paper proposes a novel many-core execution strategy for real-time model predictive controls. The key idea is to exploit predicted input values, which are produced by the model predictive control itself, to speculatively solve an op- timal control problem. It is well known that control appli- cations are not suitable for multi- or many-core processors, because feedback-loop systems inherently stand on sequen- tial operations. Since the proposed scheme does not rely on conventional thread-/data-level parallelism, it can be easily applied to such control systems. An analytical evaluation using a real application demonstrates the potential of per- formance improvement achieved by the proposed speculative executions..