  In recent years, convolutional neural network has gained popularity in many
engineering applications especially for computer vision. In order to achieve
better performance, often more complex structures and advanced operations are
incorporated into the neural networks, which results very long inference time.
For time-critical tasks such as autonomous driving and virtual reality,
real-time processing is fundamental. In order to reach real-time process speed,
a light-weight, high-throughput CNN architecture namely RoadNet-RT is proposed
for road segmentation in this paper. It achieves 90.33% MaxF score on test set
of KITTI road segmentation task and 8 ms per frame when running on GTX 1080
GPU. Comparing to the state-of-the-art network, RoadNet-RT speeds up the
inference time by a factor of 20 at the cost of only 6.2% accuracy loss. For
hardware design optimization, several techniques such as depthwise separable
convolution and non-uniformed kernel size convolution are customized designed
to further reduce the processing time. The proposed CNN architecture has been
successfully implemented on an FPGA ZCU102 MPSoC platform that achieves the
computation capability of 83.05 GOPS. The system throughput reaches 327.9
frames per second with image size 1216x176.
