| Project/Area Number |
01460152
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| Research Category |
Grant-in-Aid for General Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
計算機工学
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| Research Institution | Kyushu University |
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Principal Investigator |
AMAMIYA Makoto Kyushu University, Dept. of Information Systems, Professor, 大学院総合理工学研究科, 教授 (90202697)
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| Co-Investigator(Kenkyū-buntansha) |
KUSAKABE Shigeru Kyushu University, Dept. of Information Systems, Research Associate, 大学院総合理工学研究科, 助手 (70234416)
TSURUTA Naoyuki Kyushu University, Dept. of Information Systems, Research Associate, 大学院総合理工学研究科, 助手 (60227478)
YOSHIDA Norihiko Kyushu University, Dept. of Computer Science and Communication Engineering, Ass, 工学部情報工学科, 助教授 (00182775)
TANIGUCHI Rin-ichiro Kyushu University, Dept. of Information Systems Assistant Professor, 大学院総合理工学研究科, 助教授 (20136550)
河口 英二 九州工業大学, 工学部, 教授 (90038000)
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| Project Period (FY) |
1989 – 1991
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| Project Status |
Completed (Fiscal Year 1991)
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| Budget Amount *help |
¥5,900,000 (Direct Cost: ¥5,900,000)
Fiscal Year 1991: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 1990: ¥1,300,000 (Direct Cost: ¥1,300,000)
Fiscal Year 1989: ¥3,500,000 (Direct Cost: ¥3,500,000)
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| Keywords | Massively Parallel Multi-Processor / Data flow / Autonomous Multi-Processor System / Neural Network / Image Processing / Image Understanding / Functional Language / Object Oriented Language / 信号処理 / 自然言語解析 / 計算機ア-キテクチャ / 並列計算機 / 並列処理 / デ-タフロ-プロセッサ / プログラミング言語 |
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| Research Abstract |
In this research, we have proposed a massively parallel Autonomous Multi-Processor (AMP) for computer vision and neural networks. the system has about 10^5 processor elements (PE's) connected to one another via communication network. Each PE runs asynchronously based on a data flow control mechanism. In the data flow control mechanism, all operations are executed in parallel, according to data dependency, whether they are executed in the same processor or not. This simplifies to implement neural networks and computer vision models in a massively parallel way.
In this research, we have estimated the performance of the AMP system using a software simulator which simulates the system at the register transfer level. This shows that the system provides a high computation performance based on the high parallelism for not only low level image processing but for neural networks and intermediate level image processing. Also we have proposed a programming language based on functional programming language, which improves the software productivity for large scale applications.
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