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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| PRMU | 2022-10-21 15:25 |
Tokyo | Miraikan - The National Museum of Emerging Science and Innovation (Primary: On-site, Secondary: Online) |
Features and Deep Learning Models Suitable for Speech Source Discrimination Method in Plural Voice User Interfaces Environment Kengo Maeda, Takahiro Yoshida (TUS)  PRMU2022-27 |
Under the situation that plural devices equipped with a voice user interface exist in the user’s environment in the near... [more] | PRMU2022-27 pp.29-34 |
| CPSY, DC, IPSJ-ARC [detail] | 2022-10-11 16:15 |
Niigata | Yuzawa Toei Hotel (Primary: On-site, Secondary: Online) |
Low power quantized neural network by reducing the operating voltage of SRAM Ji Wu, Kazuteru Namba (Chiba Univ) CPSY2022-20 DC2022-20 |
With the advancement of artificial intelligence technologies, neural networks have been attracting attention as a machin... [more] | CPSY2022-20 DC2022-20 pp.14-19 |
| NC, MBE (Joint) |
2022-09-29 10:25 |
Miyagi | Tohoku Univ. (Primary: On-site, Secondary: Online) |
Analog circuit implementation of spiking neural networks and its application to time-series information processing Satoshi Moriya, Hideaki Yamamoto (Tohoku Univ), Yasushi Yuminaka (Gunma Univ.), Shigeo Sato, Yoshihiko Horio (Tohoku Univ) NC2022-33 |
Edge computing in which low-dimensional signals such as sensor output are processed nearby sensors have become increasin... [more] | NC2022-33 p.5 |
| RECONF | 2022-09-08 10:10 |
Aichi | emCAMPUS STUDIO (Primary: On-site, Secondary: Online) |
Proposal and evaluation of Combined Posit MAC unit (CPMAC) for both DNN inference and training Yuta Masuda, Yasuhiro Nakahara, Masato Kiyama, Masahiro Iida (Kumamoto Univ.) RECONF2022-34 |
Recently, there has been a lot of research on DNN hardware accelerators for the edge that use Posit as a number represen... [more] | RECONF2022-34 pp.29-34 |
| NLP | 2022-08-02 15:05 |
Online | Online | A fundamental virtual clinical trial of neural prosthetic device based on ergodic cellular automaton neuron model Haruto Suzuki, Torikai Hiroyuki (Hosei Univ.) NLP2022-35 |
In this paper, an ergodic cellular automaton neuron model and its differentiation method are proposed. It is shown that... [more] |
NLP2022-35 pp.35-38 |
| IT | 2022-07-22 10:50 |
Okayama | Okayama University of Science (Primary: On-site, Secondary: Online) |
A study on deep learning-based cyber attack detection Ruei-Fong Hong, Qiangfu Zhao (UoA), Shih-Cheng Horng (CYUT) IT2022-22 |
Cyberattack is a broad term for cybercrime that includes any deliberate attack on a computer device, network or infrastr... [more] | IT2022-22 pp.36-41 |
| CCS | 2022-03-27 14:50 |
Hokkaido | RUSUTSU RESORT HOTEL & CONVENTION (Primary: On-site, Secondary: Online) |
Examination of neuromorphic circuit architecture consisting of 3D wiring memory devices Naruki Hagiwara, Yoshiki Amemiya, Ali Emiliano Jose, Tetsuya Asai (Hokkaido Univ.), Megumi Akai-Kasaya (Hokkaido Univ./Osaka Univ.) CCS2021-46 |
Analog neural network circuits using non-volatile analog resistance change memory devices have been developed mainly bas... [more] | CCS2021-46 pp.60-65 |
| CAS, CS | 2022-03-04 15:55 |
Online | Online | Federated Learning with Correlated Sensing Data in Wireless Networks Keita Hibari, Masaya Kambara, Tomotaka Kimura, Jun Cheng (Doshisha Univ) CAS2021-99 CS2021-101 |
Federated leaning in wireless networks is considered where devices are gathered in several locations and the sensing dat... [more] | CAS2021-99 CS2021-101 pp.136-140 |
| RCS, SR, SRW (Joint) |
2022-03-04 16:25 |
Online | Online | Behavioral Modeling of RF Power Amplifiers using Binary Neural Network Taishi Watanabe, Takeo Ohseki, Kosuke Yamazaki (KDDI Research) RCS2021-293 |
With the increase in bandwidth in mobile communication systems, the effect of performance degradation due to nonlinear d... [more] | RCS2021-293 pp.207-211 |
| RECONF, VLD, CPSY, IPSJ-ARC, IPSJ-SLDM [detail] | 2022-01-24 15:55 |
Online | Online | Accelerating Deep Neural Networks on Edge Devices by Knowledge Distillation and Layer Pruning Yuki Ichikawa, Akira Jinguji, Ryosuke Kuramochi, Hiroki Nakahara (Titech) VLD2021-58 CPSY2021-27 RECONF2021-66 |
A deep neural network (DNN) is computationally expensive, making it challenging to run DNN on edge devices. Therefore, m... [more] | VLD2021-58 CPSY2021-27 RECONF2021-66 pp.49-54 |
| NLP, MICT, MBE, NC (Joint) [detail] |
2022-01-23 09:50 |
Online | Online | Analog-circuit design of STDP learning rule with linear decay and its LSI implementation Satoshi Moriya, Tatsuki Kato (Tohoku Univ.), Yasushi Yuminaka (Gunma Univ.), Hideaki Yamamoto, Shigeo Sato, Yoshihiko Horio (Tohoku Univ.) NC2021-40 |
Spiking neural networks (SNNs) are expected to be the next generation of information processing technology to reduce the... [more] | NC2021-40 p.44 |
| SDM | 2021-11-12 16:45 |
Online | Online | Simulation of Phonon Transport in Si Nanowires with Physics Informed Neural Networks Yuma Fujita, Yuhei Suzuki, Yoshinari Kamakura (Osaka Inst. Technol.) SDM2021-66 |
The temperature distribution was calculated for Si with a dogbone structure by solving the phonon-Boltzmann transport eq... [more] | SDM2021-66 pp.72-76 |
| MBE, NC (Joint) |
2021-10-28 15:05 |
Online | Online | Enhancement of spatio-temporal coding performance in spiking neural network and its application to hazard detection for landing of spacecrafts Hideaki Kinoshita, Shinichi Kimura (TUS), Seisuke Fukuda (JAXA) NC2021-21 |
Spiking neural networks (SNNs) are a neuromimetic computational architecture that has attracted much attention in recent... [more] | NC2021-21 pp.16-21 |
| CPSY, DC, IPSJ-ARC [detail] | 2021-07-21 15:45 |
Online | Online | A Domain Adaptation Method using Light-Weight Neural ODE for Low-Cost FPGAs Hiroki Kawakami, Hirohisa Watanabe, Keisuke Sugiura, Hiroki Matsutani (Keio Univ.) CPSY2021-10 DC2021-10 |
(To be available after the conference date) [more] | CPSY2021-10 DC2021-10 pp.53-58 |
| SC | 2021-05-28 15:15 |
Online | Online | Local-location and short-term solar power forecast system for marine environment observation devices Tetsuo Imai (Hiroshima City Univ.), Satoshi Ichimaru, Kenichi Arai, Toru Kobayashi (Nagasaki Univ.) SC2021-8 |
Marine environmental observatories for environmental assessment offshore are powered by renewable energy sources such as... [more] | SC2021-8 pp.39-43 |
| CCS | 2021-03-29 13:25 |
Online | Online | Neuromorphic Devices using Spatial Free Wiring of Conductive Polymer for Hardware Artificial Neural Networks Emiliano Ali, Yoshiki Amemiya, Tetsuya Asai (Hokkaido Univ.), Megumi Akai-Kasaya (Osaka Univ.) CCS2020-22 |
Nanowires made of conductive polymer have a promising potential to be used in a wide range of applications in the electr... [more] | CCS2020-22 pp.7-12 |
| CPSY, DC, IPSJ-SLDM, IPSJ-EMB, IPSJ-ARC [detail] | 2021-03-26 15:20 |
Online | Online | A Neural ODE Based Domain Adaptation Method for Edge Devices Hiroki Kawakami, Hirohisa Watanabe, Hiroki Matsutani (Keio Univ.) CPSY2020-67 DC2020-97 |
(To be available after the conference date) [more] | CPSY2020-67 DC2020-97 pp.103-108 |
| MBE, NC (Joint) |
2020-12-18 15:15 |
Online | Online | A Study on Recall of Temporal Pattern Using Hopfield Network with Pulse-type Hardware Neuron Model Yoshiki Sasaki, Katsutoshi Saeki (Nihon Univ.) NC2020-29 |
Previously, we proposed a pulse-type hardware chaotic neuron model.In this paper, we report on a pulsed neural network t... [more] | NC2020-29 pp.7-12 |
| EID, SDM, ITE-IDY [detail] | 2020-12-02 11:25 |
Online | Online | Stacked cross-point memory of synaptic elements using IGZO thin film Etsuko Iwagi (RU), Takumi Tsuno (NAIST), Mutsumi Kimura (RU) EID2020-4 SDM2020-38 |
We conducted research and development of a large hardware neural network by using oxide semiconductors of In-Ga-Zn-O (IG... [more] | EID2020-4 SDM2020-38 pp.13-16 |
| EID, SDM, ITE-IDY [detail] | 2020-12-02 11:55 |
Online | Online | Investigation of oxide semiconductor thin film synapse using STDP learning method Tetsuya Katagiri, Daiki Yamakawa, Kenta Yatida, Kazuki Morigaki, Mutsumi Kimura (Ryukoku Univ.) EID2020-6 SDM2020-40 |
Neuromorphic hardware is expected as low power consumption and high performance hardware that does not have the power co... [more] | EID2020-6 SDM2020-40 pp.21-24 |
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