低成本模块化平台实现车载高光谱图像实时分割
Rapid Deployment of Domain-specific Hyperspectral Image Processors with Application to Autonomous Driving
- 将高精度高光谱分割模型适配到低功耗嵌入式平台
- 通过量化技术在不损失精度前提下降低计算与存储开销
- 适合自动驾驶系统对实时性与成本的双重需求
本文探讨了利用低成本系统级模块（SOM）平台实现高效高光谱成像（HSI）处理器在自动驾驶中的部署。针对资源与功耗受限的嵌入式设备，研究如何在低延迟条件下实现车载图像语义分割，特别聚焦于将先前在高端异构多核片上系统（MPSoC）上训练成功的轻量级全卷积网络（FCN）重新设计并适配至低成本SOM平台。该SOM采用性能较低但成本显著更低的MPSoC，适用于自动驾驶系统（ADS）部署。文章详细描述了为适应商用定点可编程人工智能协处理器IP而采用的数据与硬件特定量化技术，并提出一种完整的后训练量化方案，有效降低计算与存储成本，同时保持分割精度。
原文摘要 · Abstract (English)
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.
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