Abstract
Reservoir computing (RC) has been viewed as a model of a neurological system. The RC framework constructs a recurrent neural network, which mimics parts of the brain, to solve temporal problems. To construct a neural network inside a reservoir, we adopt the pulse-coupled phase oscillator (PCPO) with neighbor topology connections on a field-programmable gate array (FPGA). Neural spikes for the PCPO are generated by the Winfree model. The low resource consumption of the proposed model in time-series generation tasks was confirmed in an evaluation study. We also demonstrate that on the FPGA, we can expand a 3 \(\times \) 3 PCPO into a 10 \(\times \) 10 PCPO, generate spike behavior, and predict the target signal with a maximum frequency of 418.796 MHz.
Supported by JSPS KAKENHI grant number 17H01798.
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Acknowledgment
This research is supported by JSPS KAKENHI grant number 17H01798. This paper was partly based on results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO), Japan.
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Pramanta, D., Tamukoh, H. (2020). Design and Implementation of Pulse-Coupled Phase Oscillators on a Field-Programmable Gate Array for Reservoir Computing. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1333. Springer, Cham. https://doi.org/10.1007/978-3-030-63823-8_39
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