Abstract
This paper discusses FPGA implementation of a convolutional neural network (CNN) for surgical image segmentation, which is part of a project to develop an automatic endoscope manipulation robot for laparoscopic surgery. From a viewpoint of hardware design, the major challenge to be addressed is that simple parallel implementation with spatial expansion requires a huge amount of FPGA resources. To cope with this problem, we propose a highly efficient implementation approach focusing on the recursive structure of the proposed network. Experimental results showed that the dominant computing resources could be reduced by about half in exchange for a 6% increase in memory resources and a 0.01% increase in latency. It was also observed that the operations performed on the network itself did not change, keeping the same inference results and throughput.
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Miura, T., Abe, S., Manabe, T., Shibata, Y., Kosaka, T., Adachi, T. (2023). Efficient FPGA Implementation of a Convolutional Neural Network for Surgical Image Segmentation Focusing on Recursive Structure. In: Barolli, L. (eds) Complex, Intelligent and Software Intensive Systems. CISIS 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 176. Springer, Cham. https://doi.org/10.1007/978-3-031-35734-3_14
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DOI: https://doi.org/10.1007/978-3-031-35734-3_14
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