Paper Info
Title
A Dataflow Pipelining Architecture for Tile Segmentation with a Sparse MobileNet on an FPGA
Abstract
Implementation of fast semantic segmentation in an embedded system is necessary due to the increasing interest in automatic driving and energy-efficiency is a fundamental metric in such a scenario. Because high-resolution images are required to achieve high segmentation accuracy, its accelerator must prepare large buffers for the corresponding feature maps, and it is not suitable for the limited on-chip memory of an FPGA. To address this, we propose a tile segmentation algorithm and develop an FPGA-based accelerator for a sparse MobileNet-based PSPNet. To reduce the buffer size, the tile segmentation algorithm splits incoming images into the numbers determined by the stride on an FPGA. The PSPNet then performs many split tiles. Moreover, we propose a pipelined sparse convolutional circuit to compute multiple tiles with high-speed. We compared the proposed FPGA-based system with the NVIDIA RTX 2080 Ti using the Cityscapes benchmark. The FPGA achieved 139 FPS with 24 W power consumption for a 1024×512 image, and its accuracy (mIoU) was 64.2%. Compared with the GPU, it was 1.5 times faster, its power consumption was 9.3 times lower, and its performance per power consumption was 13.8 times better.
Year
DOI
Venue
2019
10.1109/ICFPT47387.2019.00044
2019 International Conference on Field-Programmable Technology (ICFPT)
Keywords
Field
DocType
Semantic Segmentation,Convolutional Neural Network,Deep Learning,Sparseness CNN,Dataflow pipeline architecture,Tile segmentation
Pipeline (computing),Architecture,Convolutional neural network,Computer science,Segmentation,Parallel computing,Field-programmable gate array,Dataflow,Artificial intelligence,Deep learning,Tile
Conference
ISBN
Citations 
PageRank 
978-1-7281-2944-0
0
0.34
References 
Authors
5
4
Name
Order
Citations
PageRank
Youki Sada111.79
Masayuki Shimoda286.45
Akira Jinguji354.18
Hiroki Nakahara415537.34