  Semantic segmentation has been a major topic in research and industry in
recent years. However, due to the computation complexity of pixel-wise
prediction and backpropagation algorithm, semantic segmentation has been
demanding in computation resources, resulting in slow training and inference
speed and large storage space to store models. Existing schemes that speed up
segmentation network change the network structure and come with noticeable
accuracy degradation. However, neural network quantization can be used to
reduce computation load while maintaining comparable accuracy and original
network structure. Semantic segmentation networks are different from
traditional deep convolutional neural networks (DCNNs) in many ways, and this
topic has not been thoroughly explored in existing works. In this paper, we
propose a new quantization framework for training and inference of segmentation
networks, where parameters and operations are constrained to 8-bit
integer-based values for the first time. Full quantization of the data flow and
the removal of square and root operations in batch normalization give our
framework the ability to perform inference on fixed-point devices. Our proposed
framework is evaluated on mainstream semantic segmentation networks like
FCN-VGG16 and DeepLabv3-ResNet50, achieving comparable accuracy against
floating-point framework on ADE20K dataset and PASCAL VOC 2012 dataset.
