Mineto Tsukada

CS Ph.D. candidate @ Matsutani edgeAI Lab

GitHub,Google Scholar

WORK EXPERIENCES

  • Japan Society for the Promotion of Science (Apr. 2020 - Mar. 2023)

    Chiyoda-ku, Tokyo, Japan

    Research Fellow DC1

  • Ghelia Inc. (Jun. 2017 - Feb. 2023)

    Taito-ku, Tokyo, Japan

    Machine Learning Engineer (Part-Time)

  • Intel Corp. (Jun. 2018 - Aug. 2018)

    Chiyoda-ku, Tokyo, Japan

    Software Engineer (Intern)

EDUCATIONS

  • Ph.D. in Engineering (Apr. 2020 - Mar. 2023)

    Center for Information and Computer Science, Graduate School of Science and Technology, Keio University

  • Master of Engineering (Apr. 2018 - Mar. 2020)

    Center for Information and Computer Science, Graduate School of Science and Technology, Keio University

  • Bachelor of Engineering (Apr. 2014 - Mar. 2018)

    Department of Information and Computer Science, Faculty of Science and Technology, Keio University

PUBLICATIONS

Journal Papers

  • Mineto Tsukada and Hiroki Matsutani, "An Overflow/Underflow-Free Fixed-Point Bit-Width Optimization Method for OS-ELM Digital Circuit", IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, Special Section on VLSI Design and CAD Algorithms, Vol.E105-A, No.3, pp.437-447, Mar. 2022.
  • Mineto Tsukada, Masaaki Kondo and Hiroki Matsutani, "A Neural Network-Based On-device Learning Anomaly Detector for Edge Devices", IEEE Transactions on Computers (TC), Vol.69, No.7, pp.1027-1044, Jul. 2020. (Spotlight Paper in July 2020 Issue of IEEE TC)
  • Mineto Tsukada, Masaaki Kondo and Hiroki Matsutani, "OSUAD: An FPGA-Based Online Sequential Learning Unsupervised Anomaly Detector", IPSJ Transactions on Advanced Computing Systems (ACS), Vol.12, No.3, pp.34-45, Jul. 2019.
  • Rei Ito, Mineto Tsukada and Hiroki Matsutani, "An On-Device Federated Learning Approach for Cooperative Model Update between Edge Devices", IEEE Access, Vol.9, pp.92986-92998, Jun. 2021.

Conference Proceedings

  • Mineto Tsukada, Masaaki Kondo and Hiroki Matsutani, "OS-ELM-FPGA: An FPGA-Based Online Sequential Unsupervised Anomaly Detector", Proc. of the 24th International European Conference on Parallel and Distributed Computing (Euro-Par'18) Workshops, pp.518-529, Aug. 2018.
  • Hirohisa Watanabe, Mineto Tsukada and Hiroki Matsutani, "An FPGA-Based On-Device Reinforcement Learning Approach using Online Sequential Learning", Proc. of the 35th IEEE International Parallel and Distributed Processing Symposium (IPDPS'21) Workshops, The 28th Reconfigurable Architectures Workshop (RAW'21), pp.96-103, May. 2021.
  • Tokio Kibata, Mineto Tsukada and Hiroki Matsutani, "An Edge Attribute-wise Partitioning and Distributed Processing of R-GCN using GPUs", Proc. of the 26th International European Conference on Parallel and Distributed Computing (Euro-Par'20) Workshops, The 18th International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Platforms (HeteroPar'20), pp.122-134, Aug. 2020.
  • Rei Ito, Mineto Tsukada, Masaaki Kondo and Hiroki Matsutani, "An Adaptive Abnormal Behavior Detection using Online Sequential Learning", Proc. of the 17th International Conference on Embedded and Ubiquitous Computing (EUC'19), pp.436-440, Aug. 2019.
  • Tomoya Itsubo, Mineto Tsukada and Hiroki Matsutani, "Performance and Cost Evaluations of Online Sequential Learning and Unsupervised Anomaly Detection Core", Proc. of the 22nd IEEE Symposium on Low-Power and High-Speed Chips and Systems (COOL Chips 22), pp.1-3, Apr. 2019.
  • Kaho Okuyama, Yuta Tokusashi, Takuma Iwata, Mineto Tsukada, Kazumasa Kishiki and Hiroki Matsutani, "Network Optimizations on Prediction Server with Multiple Predictors", Proc. of the 16th IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA'18), pp.1044-1045, Dec. 2018

Demonstration

  • Mineto Tsukada, Masaaki Kondo and Hiroki Matsutani, "An FPGA-based On-device Sequential Learning Approach for Unsupervised Anomaly Detection", The 27th IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM'19), Demo Night, Apr. 2019.

Technical Reports

  • Mineto Tsukada, Masaaki Kondo and Hiroki Matsutani, "Anomaly Detection using On-Device Learning Algorithm on Wireless Sensor Nodes", IEICE Technical Reports CPSY2022-10 (SWoPP'22), Vol.122, No.133, pp.53-58, Jul. 2022. (IPSJ ARC Young Researcher Encouragement Award)
  • Mineto Tsukada and Hiroki Matsutani, ``Automated Fixed-Point Bit-Length Optimization for OS-ELM'', IEICE Technical Reports CPSY2020-4 (SWoPP'20), Vol.120, No.121, pp.23-28, Jul. 2020.
  • Mineto Tsukada, Masaaki Kondo and Hiroki Matsutani, "A Method for Improving Accuracy using Multiple Online Unsupervised Anomaly Detection Cores", IEICE Technical Reports CPSY2018-114 (ETNET'19), Vol.118, No.514, pp.247-252, Mar. 2019.
  • Mineto Tsukada, Masaaki Kondo and Hiroki Matsutani, "A Stable and Efficient Learning Method for FPGA-Based Online Sequential Unsupervised Anomaly Detector", IEICE Technical Reports CPSY2018-30 (SWoPP'18), Vol.118, No.165, pp.217-222, Aug. 2018. (IPSJ ARC Young Researcher Encouragement Award)
  • Mineto Tsukada, Koya Mitsuzuka, Kohei Nakamura, Yuta Tokusashi and Hiroki Matsutani, "Accelerating Sequential Learning Algorithm OS-ELM Using FPGA-NIC", IEICE Technical Reports CPSY2017-127, Vol.117, No.378, pp.133-138, Jan. 2018. (IEICE CPSY Young Presentation Award)
  • Rei Ito, Mineto Tsukada and Hiroki Matsutani, "An Efficient Cooperative Model Update using On-Device Learning", IEICE Technical Reports CPSY2019-65, Vol.119, No.372, pp.79-84, Jan. 2020.
  • Hirohisa Watanabe, Mineto Tsukada and Hiroki Matsutani, "A Light-Weight Reinforcement Learning using Online Sequential Learning", IEICE Technical Reports CPSY2019-66, Vol.119, No.372, pp.85-90, Jan. 2020.
  • Tomoya Itsubo, Mineto Tsukada and Hiroki Matsutani, "Area and Performance Evaluations of Online Sequential Learning and Unsupervised Anomaly Detection Core", IEICE Technical Reports CPSY2018-96, Vol.118, No.431, pp.83-88, Jan. 2019. (IEICE CPSY Young Presentation Award)
  • Rei Ito, Mineto Tsukada, Masaaki Kondo and Hiroki Matsutani, "A Case for Unsupervised Abnormal Behavior Detection Using Multiple Online Sequential Learning Cores", IEICE Technical Reports CPSY2018-95, Vol.118, No.431, pp.77-82, Jan. 2019.
  • Kaho Okuyama, Takuma Iwata, Mineto Tsukada, Masakazu Kishiki and Hiroki Matsutani, "FPGA and DPDK-Based Communication Acceleration Methods for Prediction Server with Multiple Predictors", IEICE Technical Reports CPSY2018-5 (HotSPA'18), Vol.118, No.92, pp.101-106, Jun. 2018.