Faculty Profiles - EDAHIRO Masato

Papers - EDAHIRO Masato

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Model-based Parallelization for Simulink Models on Multicore CPUs and GPUs Reviewed

Z. Zhong and M. Edahiro

International Journal of Computer & Technology Vol. 20 page： 1-13 2020.1

Model-Based Parallelizer for Embedded Control Systems on Single-ISA Heterogeneous Multicore Processors Reviewed

Z. Zhong, M. Edahiro

International Journal of Computer & Technology Vol. 19 page： 7470-7484 2019.2

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 Reviewed

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) page：ポスター(26) 2017.8

Parallel Design of Feedback Control Systems Utilizing Dead Time for Embedded Multicore Processors Reviewed

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

IEICE TRANSACTIONS on Electronics Vol. E99-C ( 4 ) page： 491-502 2016.4

Establishing a standard interface between multi-manycore and software tools - SHIM Reviewed

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

Proceedings of COOL Chips XVII page： VI-1 2014.4

Research on highly parallel embedded control system design and implementation method Invited

Masato Edahiro and Masaki Gondo

Impact Vol. 2019 ( 10 ) page： 44-46 2019.12

Quality Evaluation for 3D Point Cloud Maps Using Entropy Focused on Road Surfaces

KITSUKAWA Yuki, FUKAGAWA Minato, MEGURO Junichi, KATO Shinpei, EDAHIRO Masato

Journal of the Japan Society for Precision Engineering Vol. 91 ( 3 ) page： 390 - 396 2025.3

Language：Japanese Publisher：The Japan Society for Precision Engineering

Current techniques for assessing the accuracy of 3D point cloud maps are often computationally demanding and lack the capability to pinpoint regions with diminished accuracy. In this research, we introduce an effective approach for evaluating the accuracy of 3D point cloud maps intended for autonomous driving systems. Our method focuses on calculating the entropy of the road surface along the vehicle's trajectory. By employing a moving average of Mean Map Entropy (MME), we can automatically identify areas where the map accuracy has degraded while also reducing the computational load. Through our evaluation, we demonstrate that the proposed method effectively detects point cloud blurring and surface duplication caused by SLAM/MMS errors.

DOI： 10.2493/jjspe.91.390

CiNii Research

Multi-Architecture Halide Template-Driven Automatic Library Function Generation for Simulink Models Reviewed

Li, Q; Wu, SW; Edahiro, M

IEEE ACCESS Vol. 13 page： 42866 - 42873 2025

Template-Based Automatic Library Function Generation with Halide for Compute-Intensive Simulink Models Reviewed

Li, Q; Edahiro, M

2024 IEEE SYMPOSIUM IN LOW-POWER AND HIGH-SPEED CHIPS, COOL CHIPS 27 2024.4

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

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

IEEE ACCESS Vol. 12 page： 35779 - 35795 2024.3

GPU-Accelerated 3D Normal Distributions Transform

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

Journal of Robotics and Mechatronics Vol. 35 ( 2 ) page： 445 - 459 2023.4

Language：English Publisher：Fuji Technology Press Ltd.

The three-dimensional (3D) normal distributions transform (NDT) is a popular scan registration method for 3D point cloud datasets. It has been widely used in sensor-based localization and mapping applications. However, the NDT cannot entirely utilize the computing power of modern many-core processors, such as graphics processing units (GPUs), because of the NDT’s linear nature. In this study, we investigated the use of NVIDIA’s GPUs and their programming platform called compute unified device architecture (CUDA) to accelerate the NDT algorithm. We proposed a design and implementation of our GPU-accelerated 3D NDT (GPU NDT). Our methods can achieve a speedup rate of up to 34 times, compared with the NDT implemented in the point cloud library (PCL).

DOI： 10.20965/jrm.2023.p0445

Web of Science

CiNii Research

An ILP Task Mapping for MIMD Processor with Vector Accelerator in Model-Based Development Reviewed International journal

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

2022 International Conference on Electrical, Computer and Energy Technologies (ICECET) page： 1 - 7 2022.7

LLVM Instruction Latency Measurement for Software-Hardware Interface for Multi-many-core Reviewed International journal

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

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY Vol. 22 page： 50 - 63 2022.5

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

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

2022 INTERNATIONAL POWER ELECTRONICS CONFERENCE (IPEC-HIMEJI 2022- ECCE ASIA) page： 1314 - 1319 2022.5

Single-Shot Intrinsic Calibration for Autonomous Driving Applications Reviewed

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

Vol. 22 ( 5 ) 2022.3

Mapping Method Usable with Clustered Many-core Platforms for Simulink Model Reviewed

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

Journal of Information Processing Vol. 30 ( 0 ) page： 141 - 150 2022.2

SHIM Standard for Control System Design on Multi-Many-Core SoC Reviewed

Edahiro Masato, Gondo Masaki

SYSTEMS, CONTROL AND INFORMATION Vol. 66 ( 1 ) page： 21 - 26 2022.1

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 Vol. 33 ( 3 ) page： 686 - 697 2021.6

Language：English Publisher：Fuji Technology Press Ltd.

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.

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 Vol. 32 ( 3 ) page： 548 - 560 2020.6

An Open Multi-Sensor Fusion Toolbox for Autonomous Vehicles

MONRROY CANO Abraham, TAKEUCHI Eijiro, KATO Shinpei, EDAHIRO Masato

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences Vol. E103.A ( 1 ) page： 252 - 264 2020.1

Language：English Publisher：The Institute of Electronics, Information and Communication Engineers

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.

DOI： 10.1587/transfun.2019tsp0005

Web of Science

CiNii Research

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Papers - EDAHIRO Masato