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Committee Date Time Place Paper Title / Authors Abstract Paper #
SDM 2023-11-10
14:00		 Tokyo
(Primary: On-site, Secondary: Online)		 Examination of high high-precision device modeling methods -- Comparison of Neural Networks and Linear Regression --
Kengo Nakata, Takayuki Mori, Jiro Ida (Kanazawa Inst. Tech.) SDM2023-71		 Neural network (NN) models have the advantage of high inference speed, but they are difficult to modeling. For this reas... [more] SDM2023-71
pp.36-40
SDM 2020-01-28
14:45		 Tokyo Kikai-Shinko-Kaikan Bldg. [Invited Talk] Can in-memory/Analog Accelerators be a Silver Bullet for Energy-efficient Inference?
Jun Deguchi, Daisuke Miyashita, Asuka Maki, Shinichi Sasaki, Kengo Nakata, Fumihiko Tachibana, Ryuichi Fujimoto (KIOXIA) SDM2019-85		 This presentation introduces and discuss recent trends on in-memory/analog computing for deep learning inference, which ... [more] SDM2019-85
p.11
ICD, CPSY, CAS 2017-12-15
09:30		 Okinawa Art Hotel Ishigakijima A 28GHz Fractional-N Frequency Synthesizer with Reference and Frequency Doublers for 5G New Radio
Hanli Liu, Teerachot Siriburaron, Kengo Nakata, Wei Deng, Kenichi Okada, Akira Matsuzawa (Tokyo Tech.) CAS2017-101 ICD2017-89 CPSY2017-98		 The increasing demand for larger data capacity drives researchers finding new spectrum beyond 4G, i.e., 28GHz frequency ... [more] CAS2017-101 ICD2017-89 CPSY2017-98
pp.147-150
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