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.

本文提出了一种多任务卷积神经网络 (CNN) 体系结构，旨在为低功率汽车级 SoC 进行优化，其中编码器在检测和分割两个任务之间共享，介绍了多种优化方法，如直接使用本地 YUV 图像、优化层和特征图以及应用量化等，并重点关注了内存带宽和卷积等数据密集型操作之间的瓶颈问题。最终，我们演示了我们所提出的网络在相应运行时中为检测和分割任务提供的关键性能指标 (KPI)。