Abstract
Neuromorphic hardware using simplified elements and thin-film semiconductor devices as synapse elements is proposed. It is assumed that amorphous metal-oxide semiconductor devices are used for the synapse elements, and the characteristic degradation is utilized for the learning rule named modified Hebbian learning. First, we explain an architecture and operation of a Hopfield neural network. Next, we model the electrical characteristic of the thin-film semiconductor devices and simulate the letter recognition by the neural network. Particularly in this presentation, we show a degradation map. On the other hand, we also explain an architecture and operation of a cellular neural network, model the thin-film semiconductor devices, and simulate the letter recognition. Particularly in this presentation, we evaluate connection schemes. It is found that the cellular neural network has higher performance when it has diagonal connections. Moreover, we compare the Hopfield and cellular neural networks. It is found that the Hopfield neural network has higher performance, although the cellular neural network has a simple structure.
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Acknowledgement
We would like to thank Prof. Mamoru Furuta of Kochi University of Technology, Prof. Toshio Kamiya of Tokyo Institute of Technology, KAKENHI 16K06733, Laboratory for Materials and Structures of Tokyo Institute of Technology, ROHM Semiconductor, Yazaki Memorial Foundation for Science and Technology, Support Center for Advanced Telecommunications Technology Research Foundation, and KOA Corporation.
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Kameda, T., Kimura, M., Nakashima, Y. (2017). Neuromorphic Hardware Using Simplified Elements and Thin-Film Semiconductor Devices as Synapse Elements - Simulation of Hopfield and Cellular Neural Network -. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10639. Springer, Cham. https://doi.org/10.1007/978-3-319-70136-3_81
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DOI: https://doi.org/10.1007/978-3-319-70136-3_81
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