Training Bit Fully Convolutional Network for Fast Semantic Segmentation
Abstract
Fully convolutional neural networks give accurate, per-pixel prediction for input images and have applications like semantic segmentation. However, a typical FCN usually requires lots of floating point computation and large run-time memory, which effectively limits its usability. We propose a method to train Bit Fully Convolution Network (BFCN), a fully convolutional neural network that has low bit-width weights and activations. Because most of its computation-intensive convolutions are accomplished between low bit-width numbers, a BFCN can be accelerated by an efficient bit-convolution implementation. On CPU, the dot product operation between two bit vectors can be reduced to bitwise operations and popcounts, which can offer much higher throughput than 32-bit multiplications and additions. To validate the effectiveness of BFCN, we conduct experiments on the PASCAL VOC 2012 semantic segmentation task and Cityscapes. Our BFCN with 1-bit weights and 2-bit activations, which runs 7.8x faster on CPU or requires less than 1\% resources on FPGA, can achieve comparable performance as the 32-bit counterpart.
Cite
@article{arxiv.1612.00212,
title = {Training Bit Fully Convolutional Network for Fast Semantic Segmentation},
author = {He Wen and Shuchang Zhou and Zhe Liang and Yuxiang Zhang and Dieqiao Feng and Xinyu Zhou and Cong Yao},
journal= {arXiv preprint arXiv:1612.00212},
year = {2016}
}