在嵌入式芯片上实现卫星姿态实时估计，功耗仅2瓦
Accelerating AI and Computer Vision for Satellite Pose Estimation on the Intel Myriad X Embedded SoC
- 结合深度网络与传统视觉算法，部署于Intel Myriad X芯片
- 实测对1080p图像实现最高5帧/秒的处理速度
- 适合对功耗敏感的航天器自主导航系统
将人工智能（AI）与计算机视觉（CV）算法部署于边缘设备面临巨大挑战，推动嵌入式计算领域关注异构片上系统（SoC）。此类新型计算平台虽提供多样化的接口、处理器和存储，但高效划分与映射AI/CV工作负载仍是未解难题。本文在Intel Movidius Myriad X——一种异构视觉处理单元（VPU）上构建了混合式AI/CV系统，用于空间任务中卫星姿态的初始化与跟踪。航天领域正探索替代计算平台以满足星载数据处理的严苛约束，并积极引入AI功能。算法层面，采用基于ResNet-50的UrsoNet网络结合定制经典视觉流水线；为实现高效加速，充分利用SoC的神经计算引擎与16个向量处理器，融合多种并行化与底层优化技术。所提单芯片、鲁棒估计、实时解决方案可在2瓦功耗内，实现对1兆像素RGB图像最高5帧/秒的吞吐量。
原文摘要 · Abstract (English)
The challenging deployment of Artificial Intelligence (AI) and Computer Vision (CV) algorithms at the edge pushes the community of embedded computing to examine heterogeneous System-on-Chips (SoCs). Such novel computing platforms provide increased diversity in interfaces, processors and storage, however, the efficient partitioning and mapping of AI/CV workloads still remains an open issue. In this context, the current paper develops a hybrid AI/CV system on Intel's Movidius Myriad X, which is an heterogeneous Vision Processing Unit (VPU), for initializing and tracking the satellite's pose in space missions. The space industry is among the communities examining alternative computing platforms to comply with the tight constraints of on-board data processing, while it is also striving to adopt functionalities from the AI domain. At algorithmic level, we rely on the ResNet-50-based UrsoNet network along with a custom classical CV pipeline. For efficient acceleration, we exploit the SoC's neural compute engine and 16 vector processors by combining multiple parallelization and low-level optimization techniques. The proposed single-chip, robust-estimation, and real-time solution delivers a throughput of up to 5 FPS for 1-MegaPixel RGB images within a limited power envelope of 2W.
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