Abstract
We will propose a novel architecture for a cellular neural network suitable for high-density integration of electron devices. A neuron consists of only eight transistors, and a synapse consists of just only one transistor. We fabricated a cellular neural network using thin-film devices. Particularly in this time, we confirmed that our neural network can learn multiple logics even in a small-scale neural network. We think that this result indicates that our proposal has a big potential for future electronics using neural networks.
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Acknowledgments
We thank Prof. Hakaru Tamukoh of Kyushu Institute of Technology and Prof. Yasuhiko Nakashima of Nara Institute of Science and Technology. This research is partially supported by a research project of the Joint Research Center for Science and Technology at Ryukoku University and grant from the High-Tech Research Center Program for private universities from the Ministry of Education, Culture, Sports, Science and Technology (MEXT).
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Kimura, M., Fujita, Y., Kasakawa, T., Matsuda, T. (2015). Novel Architecture for Cellular Neural Network Suitable for High-Density Integration of Electron Devices-Learning of Multiple Logics. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9489. Springer, Cham. https://doi.org/10.1007/978-3-319-26532-2_2
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DOI: https://doi.org/10.1007/978-3-319-26532-2_2
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