研究者詳細 - 枝廣　正人

論文 - 枝廣　正人

分割表示 217 件中 1 - 20 件目／全件表示 >>

Model-based Parallelization for Simulink Models on Multicore CPUs and GPUs 査読有り

Z. Zhong and M. Edahiro

International Journal of Computer & Technology 20 巻頁： 1-13 2020年1月

Model-Based Parallelizer for Embedded Control Systems on Single-ISA Heterogeneous Multicore Processors 査読有り

Z. Zhong, M. Edahiro

International Journal of Computer & Technology 19 巻頁： 7470-7484 2019年2月

モデルベース開発におけるクロスレイヤ設計手法のマルチコア上モータ制御実装への適用査読有り

竹松慎弥，鍾兆前，井上雅理，横山静香，小島流石(大阪大)，近藤真己(NECソリューションイノベータ)，中本幸一(兵庫県立大)，安積卓也(大阪大)，道木慎二，本田晋也, 枝廣正人

組込みシステムシンポジウム(ESS2017) 頁：ポスター(26) 2017年8月

Parallel Design of Feedback Control Systems Utilizing Dead Time for Embedded Multicore Processors 査読有り

Y. Suzuki (DENSO), K. Sata (Toyota), J. Kako (Toyota), K. Yamaguchi, F. Arakawa and M. Edahiro

IEICE TRANSACTIONS on Electronics E99-C 巻 ( 4 ) 頁： 491-502 2016年4月

Establishing a standard interface between multi-manycore and software tools - SHIM 査読有り

M. Gondo, F. Arakawa, and M. Edahiro

Proceedings of COOL Chips XVII 頁： VI-1 2014年4月

Research on highly parallel embedded control system design and implementation method 招待有り

Masato Edahiro and Masaki Gondo

Impact 2019 巻 ( 10 ) 頁： 44-46 2019年12月

路面に注目した点群のエントロピーを用いた三次元点群地図の品質評価手法

橘川雄樹, 深川備叶, 目黒淳一, 加藤真平, 枝廣正人

精密工学会誌 91 巻 ( 3 ) 頁： 390 - 396 2025年3月

Multi-Architecture Halide Template-Driven Automatic Library Function Generation for Simulink Models 査読有り

Li, Q; Wu, SW; Edahiro, M

IEEE ACCESS 13 巻頁： 42866 - 42873 2025年

Template-Based Automatic Library Function Generation with Halide for Compute-Intensive Simulink Models 査読有り

Li, Q; Edahiro, M

2024 IEEE SYMPOSIUM IN LOW-POWER AND HIGH-SPEED CHIPS, COOL CHIPS 27 2024年4月

Task Mapping and Scheduling on RISC-V MIMD Processor With Vector Accelerator Using Model-Based Parallelization 査読有り

Wu, SW; Kumano, S; Marume, K; Edahiro, M

IEEE ACCESS 12 巻頁： 35779 - 35795 2024年3月

GPU-Accelerated 3D Normal Distributions Transform

Nguyen, A; Cano, AM; Edahiro, M; Kato, S

Journal of Robotics and Mechatronics 35 巻 ( 2 ) 頁： 445 - 459 2023年4月

An ILP Task Mapping for MIMD Processor with Vector Accelerator in Model-Based Development 査読有り国際誌

S. Wu, S. Kumano, K. Marume and M. Edahiro

2022 International Conference on Electrical, Computer and Energy Technologies (ICECET) 頁： 1 - 7 2022年7月

LLVM Instruction Latency Measurement for Software-Hardware Interface for Multi-many-core 査読有り国際誌

H. Mikami, K. Torigoe, M. Inokawa, and M. Edahiro

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 22 巻頁： 50 - 63 2022年5月

Implementation of Vector Control System On Multi-Core Processor by Using Model-Based Parallelization Tool 査読有り

Kim Jinsoo, Sagae Shota, Edahiro Masato, Honda Shinya, Doki Shinji

2022 INTERNATIONAL POWER ELECTRONICS CONFERENCE (IPEC-HIMEJI 2022- ECCE ASIA) 頁： 1314 - 1319 2022年5月

Single-Shot Intrinsic Calibration for Autonomous Driving Applications 査読有り

Monrroy Cano Abraham, Lambert Jacob, Edahiro Masato, Kato Shinpei

22 巻 ( 5 ) 2022年3月

Mapping Method Usable with Clustered Many-core Platforms for Simulink Model 査読有り

Kobayashi Yutaro, Honda Kentaro, Kojima Sasuga, Fujimoto Hiroshi, Edahiro Masato, Azumi Takuya

Journal of Information Processing 30 巻 ( 0 ) 頁： 141 - 150 2022年2月

複雑化するSoC上での制御設計を支援するSHIM標準化活動査読有り

枝廣正人, 権藤正樹

システム／制御／情報 66 巻 ( 1 ) 頁： 21 - 26 2022年1月

Vision-Based Sensing Systems for Autonomous Driving: Centralized or Decentralized?

Hirabayashi Manato, Saito Yukihiro, Murakami Kosuke, Ohsato Akihito, Kato Shinpei, Edahiro Masato

Journal of Robotics and Mechatronics 33 巻 ( 3 ) 頁： 686 - 697 2021年6月

記述言語：英語出版者・発行元：富士技術出版株式会社

<p>The perception of the surrounding circumstances is an essential task for fully autonomous driving systems, but its high computational and network loads typically impede a single host machine from taking charge of the systems. Decentralized processing is a candidate to decrease such loads; however, it has not been clear that this approach fulfills the requirements of onboard systems, including low latency and low power consumption. Embedded oriented graphics processing units (GPUs) are attracting great interest because they provide massively parallel computation capacity with lower power consumption compared to traditional GPUs. This study explored the effects of decentralized processing on autonomous driving using embedded oriented GPUs as decentralized units. We implemented a prototype system that off-loaded image-based object detection tasks onto embedded oriented GPUs to clarify the effects of decentralized processing. The results of experimental evaluation demonstrated that decentralized processing and network quantization achieved approximately 27 ms delay between the feeding of an image and the arrival of detection results to the host as well as approximately 7 W power consumption on each GPU and network load degradation in orders of magnitude. Judging from these results, we concluded that decentralized processing could be a promising approach to decrease processing latency, network load, and power consumption toward the deployment of autonomous driving systems.</p>

DOI： 10.20965/jrm.2021.p0686

Web of Science

CiNii Research

Fast Euclidean Cluster Extraction Using GPUs

Anh Nguyen, Cano Abraham Monrroy, Edahiro Masato, Kato Shinpei

JOURNAL OF ROBOTICS AND MECHATRONICS 32 巻 ( 3 ) 頁： 548 - 560 2020年6月

An Open Multi-Sensor Fusion Toolbox for Autonomous Vehicles

Cano Abraham Monrroy, Takeuchi Eijiro, Kato Shinpei, Edahiro Masato

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES E103.A 巻 ( 1 ) 頁： 252 - 264 2020年1月

記述言語：英語出版者・発行元：一般社団法人電子情報通信学会

<p>We present an accurate and easy-to-use multi-sensor fusion toolbox for autonomous vehicles. It includes a 'target-less' multi-LiDAR (Light Detection and Ranging), and Camera-LiDAR calibration, sensor fusion, and a fast and accurate point cloud ground classifier. Our calibration methods do not require complex setup procedures, and once the sensors are calibrated, our framework eases the fusion of multiple point clouds, and cameras. In addition we present an original real-time ground-obstacle classifier, which runs on the CPU, and is designed to be used with any type and number of LiDARs. Evaluation results on the KITTI dataset confirm that our calibration method has comparable accuracy with other state-of-the-art contenders in the benchmark.</p>

DOI： 10.1587/transfun.2019tsp0005

Web of Science

CiNii Research

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