The article discusses the use of low cost System-On-Module (SOM) platforms for the implementation of efficient hyperspectral imaging (HSI) processors for application in autonomous driving. The work addresses the challenges of shaping and deploying multiple layer fully convolutional networks (FCN) for low-latency, on-board image semantic segmentation using resource- and power-constrained processing devices. The paper describes in detail the steps followed to redesign and customize a successfully trained HSI segmentation lightweight FCN that was previously tested on a high-end heterogeneous multiprocessing system-on-chip (MPSoC) to accommodate it to the constraints imposed by a low-cost SOM. This SOM features a lower-end but much cheaper MPSoC suitable for the deployment of automatic driving systems (ADS). In particular the article reports the data- and hardware-specific quantization techniques utilized to fit the FCN into a commercial fixed-point programmable AI coprocessor IP, and proposes a full customized post-training quantization scheme to reduce computation and storage costs without compromising segmentation accuracy.

翻译：本文探讨了利用低成本片上系统模块平台实现高效高光谱图像处理器在自动驾驶领域的应用。研究工作针对在资源和功耗受限的处理设备上，为低延迟车载图像语义分割任务构建并部署多层全卷积网络所面临的挑战。论文详细阐述了将先前在高端异构多处理器片上系统上测试成功的轻量化高光谱图像分割全卷积网络，重新设计与定制以适应低成本片上系统模块约束条件的具体步骤。该模块采用一款性能较低但成本更适宜自动驾驶系统部署的多处理器片上系统。文章特别报告了为将全卷积网络适配至商用定点可编程人工智能协处理器知识产权核所采用的数据与硬件专用量化技术，并提出一套完全定制化的训练后量化方案，在保持分割精度的同时显著降低计算与存储开销。