Abstract
Numerous studies have proven that slee** position classification plays an essential role in medical diagnosis. Currently, spiking neural networks (SNNs) are emerging as a new trend to solve this task due to its energy-saving advantage. However, none of current studies considers the hardware implementation of their proposed networks. Therefore, this paper presents a process to synthesize a spiking neural network for slee** posture classification using RANC on FPGA. To conserve hardware resources, we proposed a method called parameter reconfiguration, which could reduce the number of SNNs model cores required from 84 to 21. Experimental results confirmed that our hardware implementation achieved a high accuracy of 92.4\(\%\) and low-power consumption of 0.0167 (W) per image.
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Nguyen, V.C., Nguyen, L.T., Dam, H.P., Nguyen, D.M., Nguyen, H.H. (2023). RANC-Based Hardware Implementation of Spiking Neural Network for Slee** Posture Classification. In: Shukla, P.K., Mittal, H., Engelbrecht, A. (eds) Computer Vision and Robotics. CVR 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-4577-1_21
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DOI: https://doi.org/10.1007/978-981-99-4577-1_21
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