  In this paper, we investigate how field programmable gate arrays can serve as
hardware accelerators for real-time semantic segmentation tasks relevant for
autonomous driving. Considering compressed versions of the ENet convolutional
neural network architecture, we demonstrate a fully-on-chip deployment with a
latency of 4.9 ms per image, using less than 30% of the available resources on
a Xilinx ZCU102 evaluation board. The latency is reduced to 3 ms per image when
increasing the batch size to ten, corresponding to the use case where the
autonomous vehicle receives inputs from multiple cameras simultaneously. We
show, through aggressive filter reduction and heterogeneous quantization-aware
training, and an optimized implementation of convolutional layers, that the
power consumption and resource utilization can be significantly reduced while
maintaining accuracy on the Cityscapes dataset.
