Akira Jinguji, RIKEN, Japan

Journals (peer-reviewed)

  1. Weakly guiding approximation of a three-dimensional waveguide model for extreme ultraviolet lithography simulation (TBA)

  2. Pre-training CNN for fast EUV lithography simulation including M3D effects (TBA)

  3. H. Tanabe, A. Jinguji, A. Takahashi, "Accelerating extreme ultravolet lithography simulation with weakly guiding approximation and source position dependent transmission cross coefficient formula", Journal of Micro/Nanopatterning, Mater. Metrol (JM3), (accepted).

  4. H. Tanabe, A. Jinguji, A. Takahashi,

    "Evaluation of Convolutional Neural Network for Fast Extreme Ultraviolet Lithography Simulation using Imec 3 nm Node Mask Patterns",

    Journal of Micro/Nanopatterning, Materials, and Metrology, Vol. 22, Issue 2, 024201, June 2023.

  5. T. Senoo, A. Jinguji, R. Kuramochi, H. Nakahara,

    "A Multilayer Perceptron Training Accelerator using Systolic Array",

    IEICE Transactions on Information and Systems, Vol. E105-D, No. 12, pp.2048-2056, December 2022.

  6. A. Jinguji, S. Sato, H. Nakahara,

    "Weight Sparseness for a Feature-Map-Split-CNN Toward Low-Cost Embedded FPGAs",

    IEICE Transactions on Information and Systems, Vol. E104-D, No. 12, pp. 2040-2047, December 2021.

  7. A. Jinguji, S. Sato, H. Nakahara,

    "An FPGA Realization of a Random Forest with k-means Clustering using a High-level Synthesis Design",

    IEICE Transactions on Information and Systems, Vol. E101-D, No. 2, pp. 354-362, February 2018.

International Conferences (peer-reviewed)

  1. A. Jinguji, K. Sano, ”A Comparative Survey of GPUs and ASICs for AI Acceleration”, R-CCS Symposium 2025 (Poster).

  2. “A Many-core Architecture for an Ensemble Ternary Neural Network Toward High-Throughput Inference”, (TBA)

  3. T. Senoo, R. Kayanoma, A. Jinguji, and H. Nakahara,

    "A Light-weight Vision Transformer toward Near-Memory Computation on an FPGA",

    Applied Reconfigurable Computing International Symposium, (ARC 2023), pp. 338-353, September 2023.

  4. H. Tanabe, A. Jinguji, A. Takahashi,

    "Evaluation of CNN for fast EUV lithography simulation using iN3 logic mask patterns."

    Proc. SPIE 12495, DTCO and Computational Patterning II, 12495-55, March, 2023.

  5. T. Senoo, A. Jinguji, R. Kuramochi, H. Nakahara,

    "A Multilayer Perceptron Training Accelerator using Systolic Array",

    IEEE Asia Pacific Conf. on Circuits and Systems (APCCAS2021), pp. 77-80, Online, November 2021.

  6. Y. Sada, N. Soga, M. Shimoda, A. Jinguji, S. Sato, H. Nakahara,

    "Fast Monocular Depth Estimation on an FPGA",

    IEEE International Parallel and Distributed Processing Symposium Workshops (RAW2020), pp. 143-146, Online, May, 2020.

  7. A. Jinguji, S. Sato, H. Nakahara,

    "Tiny On-Chip Memory Realization of Weight Sparseness Split-CNNs on Low-end FPGAs",

    IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM2020), p. 229, Online, May 2020.

  8. H. Nakahara, Q. Zhiqiang, A. Jinguji, W. Luk,

    "R2CNN: Recurrent Residual Convolutional Neural Network on FPGA",

    ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA2020), p. 319, Seaside, California, USA, February 2020.