Session Details

[S2]Photonic Computing Solving the Bottleneck of Von-Neumann Computing

Tue. Jul 1, 2025 9:00 AM - 10:30 AM JST
Tue. Jul 1, 2025 12:00 AM - 1:30 AM UTC
Room A (1F Conference Hall)
Organizers: Ryan Michael Hamerly (NTT Research), Mitsumasa Nakajima (NTT Corporation), Nathan Youngblood (University of Pittsburgh)
Rapid advancement of machine learning technologies raised critical issues regarding the energy consumption for computations, which motivated various researches on alternative analog computing hardware. Photonic computing, with its unique advantages such as ultra-wide bandwidth and space/wavelength parallelism, offers a promising avenue for dramatically improving computational efficiency. Furthermore, recent breakthroughs in nanophotonics and optoelectronic integration suggest the possibility of large-scale integration of photonic computing engines. This symposium will explore the current state of optical computing integration, algorithms, and applications, discussing both its strengths and weaknesses, as well as its future prospects.

[S2-1]A Multi-mode Semiconductor Laser Photonic Neural Network and Its Training

○Romain Lance1, Anas Skalli1, Xavier Porte2, Daniel Brunner1 (1. Université Marie et Louis Pasteur, CNRS, institut FEMTO-ST (France), 2. Institute of Photonics, University of Strathclyde, Glasgow, UK (UK))
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[S2-2]Photonic Domain Transformation for High-Speed Machine Vision and Low-Latency Tactile Sensing

○Satoshi Sunada1, Kei Kitagawa1, Tomoya Yamaguchi1 (1. Kanazawa University (Japan))
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[S2-3]Photonic spiking neural network based on coupled optical parametric oscillators

○Takahiro Inagaki1, Kensuke Inaba1, Yasuhiro Yamada1, Toshimori Honjo1, Takuya Ikuta1, Yuya Yonezu1, Takushi Kazama2, Koji Enbutsu2, Takeshi Umeki2, Ryoichi Kasahara2, Kazuyuki Aihara3, Hiroki Takesue1 (1. NTT Basic Research Laboratories, NTT Corporation (Japan), 2. NTT Device Technology Laboratories, NTT Corporation (Japan), 3. International Research Center for Neurointelligence, The University of Tokyo (Japan))
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