  In this paper, we propose a multi-task convolutional neural network (CNN)
architecture optimized for a low power automotive grade SoC. We introduce a
network based on a unified architecture where the encoder is shared among the
two tasks namely detection and segmentation. The pro-posed network runs at
25FPS for 1280x800 resolution. We briefly discuss the methods used to optimize
the network architecture such as using native YUV image directly, optimization
of layers & feature maps and applying quantization. We also focus on memory
bandwidth in our design as convolutions are data intensives and most SOCs are
bandwidth bottlenecked. We then demonstrate the efficiency of our proposed
network for a dedicated CNN accelerators presenting the key performance
indicators (KPI) for the detection and segmentation tasks obtained from the
hardware execution and the corresponding run-time.
