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2021 Fiscal Year Final Research Report

Real-Time Data Kernel for Data Intensive Science

Research Project

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Project/Area Number 19H04117
Research Category

Grant-in-Aid for Scientific Research (B)

Allocation TypeSingle-year Grants
Section一般
Review Section Basic Section 60080:Database-related
Research InstitutionKeio University

Principal Investigator

KAWASHIMA Hideyuki  慶應義塾大学, 環境情報学部(藤沢), 准教授 (90407148)

Co-Investigator(Kenkyū-buntansha) 松谷 宏紀  慶應義塾大学, 理工学部(矢上), 准教授 (70611135)
藤原 靖宏  日本電信電話株式会社NTTコミュニケーション科学基礎研究所, 上田特別研究室, 主任研究員 (70837971)
Project Period (FY) 2019-04-01 – 2022-03-31
Keywordsトランザクション / 機械学習 / アクセラレータ
Outline of Final Research Achievements

In order to create a real-time data kernel for data-intensive science, we conducted research on high-performance transaction processing, fast machine learning algorithms, and fast accelerators for AI. For transaction processing, we developed CCBench, a platform that enables comprehensive comparison and evaluation of modern methods, explored superior protocols, and devised optimization methods. We also applied these methods to robotics. For machine learning, we accelerated key algorithms such as b-matching and anchor graphs. For accelerators, we have succeeded in improving the performance of DQN and other algorithms using FPGAs.

Free Research Field

小区分60080:データベース関連

Academic Significance and Societal Importance of the Research Achievements

本研究ではリアルタイムデータカーネルの創出を目的とし,トランザクション処理というコア技術を研磨した結果,ロボット用ミドルウェアROSをリアルタイム化かつ正確化する技術を開発し,また,それをソフトウェアとして創出するに至った.このような成果は我々の知る限り存在しない.機械学習アルゴリズムならびにアクセラレータについては,SOTAに優る成果を生み出せた.

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Published: 2023-01-30  

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