| Scalable reservoir computing on coherent linear photonic processor M Nakajima, K Tanaka, T Hashimoto Communications Physics 4 (1), 20, 2021 | 166 | 2021 |
| Physical deep learning with biologically inspired training method: gradient-free approach for physical hardware M Nakajima, K Inoue, K Tanaka, Y Kuniyoshi, T Hashimoto, K Nakajima Nature communications 13 (1), 7847, 2022 | 83 | 2022 |
| Neural schrödinger equation: Physical law as deep neural network M Nakajima, K Tanaka, T Hashimoto IEEE transactions on neural networks and learning systems 33 (6), 2686-2700, 2021 | 36 | 2021 |
| Communication-efficient distributed deep learning with GPU-FPGA heterogeneous computing K Tanaka, Y Arikawa, T Ito, K Morita, N Nemoto, F Miura, K Terada, ... 2020 IEEE Symposium on High-Performance Interconnects (HOTI), 43-46, 2020 | 10 | 2020 |
| Large-message size allreduce at wire speed for distributed deep learning K Tanaka, Y Arikawa, K Kawai, J Kato, T Ito, HC Ngo, K Morita, F Miura, ... Proc. Poster Session Presented at SC18, 2018 | 4 | 2018 |
| Distributed deep learning with GPU-FPGA heterogeneous computing K Tanaka, Y Arikawa, T Ito, K Morita, N Nemoto, K Terada, J Teramoto, ... IEEE Micro 41 (1), 15-22, 2020 | 3 | 2020 |
| Neural Schr\"{o} dinger Equation: Physical Law as Neural Network M Nakajima, K Tanaka, T Hashimoto arXiv preprint arXiv:2006.13541, 2020 | 1 | 2020 |
| CiraaS: cloud computing with programmable logic K Tanaka, Y Arikawa, T Ito, Y Matsuda, K Kamahori, S Kaji, T Sakamoto Proceedings of the SIGCOMM'22 Poster and Demo Sessions, 43-45, 2022 | | 2022 |
| VTA-NIC: Deep Learning Inference Serving in Network Interface Cards K Tanaka, Y Arikawa, K Morita, T Ito, T Uchida, N Saito, S Kaji, ... 2022 IEEE Hot Chips 34 Symposium (HCS), 1-16, 2022 | | 2022 |
| Photonic Reservoir Computing on Coherent Linear Processor M Nakajima, T Tsurugaya, K Tanaka, S Matsuo, T Hashimoto 2022 27th OptoElectronics and Communications Conference (OECC) and 2022 …, 2022 | | 2022 |
| Physical deep learning with biologically plausible training method M Nakajima, K Inoue, K Tanaka, Y Kuniyoshi, T Hashimoto, K Nakajima arXiv preprint arXiv:2204.13991, 2022 | | 2022 |
| With GPU-FPGA Heterogeneous computing, Highly Effective Communication for Distributed Deep Learning K Tanaka, Y Arikawa, T Ito, K Morita, N Nemoto, F Miura, K Terada, ... IEICE Technical Report; IEICE Tech. Rep. 120 (168), 1-6, 2020 | | 2020 |
| 分散深層学習を高速化させる FPGA Ring-Allreduce の検討 K TANAKA, Y ARIKAWA, T ITO, K TERADA, K MORITA, S MIURA, ... 情報処理学会全国大会講演論文集 82 (1), 31-1, 2020 | | 2020 |
