  Camera-based Deep Learning algorithms are increasingly needed for perception
in Automated Driving systems. However, constraints from the automotive industry
challenge the deployment of CNNs by imposing embedded systems with limited
computational resources. In this paper, we propose an approach to embed a
multi-task CNN network under such conditions on a commercial prototype
platform, i.e. a low power System on Chip (SoC) processing four surround-view
fisheye cameras at 10 FPS.
  The first focus is on designing an efficient and compact multi-task network
architecture. Secondly, a pruning method is applied to compress the CNN,
helping to reduce the runtime and memory usage by a factor of 2 without
lowering the performances significantly. Finally, several embedded optimization
techniques such as mixed-quantization format usage and efficient data transfers
between different memory areas are proposed to ensure real-time execution and
avoid bandwidth bottlenecks. The approach is evaluated on the hardware
platform, considering embedded detection performances, runtime and memory
bandwidth. Unlike most works from the literature that focus on classification
task, we aim here to study the effect of pruning and quantization on a compact
multi-task network with object detection, semantic segmentation and soiling
detection tasks.
