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Filter-Wise Pruning Approach to FPGA Implementation of Fully Convolutional Network for Semantic Segmentation

  • Masayuki ShimodaEmail author
  • Youki Sada
  • Hiroki Nakahara
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11444)

Abstract

This paper presents a hardware-aware sparse fully convolutional network (SFCN) for semantic segmentation on an FPGA. Semantic segmentation attracts interest since for self-driving car it is important to recognize road and obstacles in pixel level. However, it is hard to implement the system on embedded systems since the number of weights for the SFCN is so large that embedded systems cannot store them using limited on-chip memory. To realize good a trade-off between speed and accuracy, we construct an AlexNet-based SFCN which has no skip connections and deconvolution layers to reduce the computation costs and the latency. Furthermore, we propose a filter-wise pruning technique that sorts the weights of each filter by their absolute values and prunes them by a preset percent filter-by-filter from a small order. It is more suitable for the hardware implementation since the number of computation of each filter becomes equal. We trained the AlexNet-based SFCN by using Camvid image dataset and implemented on Xilinx zcu102 evaluation board. The results show that the FPGA system is 10.14 times faster than a mobile GPU one, and its performance per power consumption is 24.49 times higher than the GPU counterpart.

Keywords

FPGA Fully convolutional network Sparse neural network Semantic segmentation 

Notes

Acknowledgments

This research is supported in part by the Grants in Aid for Scientific Research from JSPS, and the New Energy and Industrial Technology Development Organization (NEDO). In addition, thanks are extended to the Xilinx University Program (XUP), the Intel University Program, and NVidia Corp. for their support.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Tokyo Institute of TechnologyTokyoJapan

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