Satellites have become more widely available due to the reduction in size and cost of their components. As a result, there has been an advent of smaller organizations having the ability to deploy satellites with a variety of data-intensive applications to run on them. One popular application is image analysis to detect, for example, land, ice, clouds, etc. for Earth observation. However, the resource-constrained nature of the devices deployed in satellites creates additional challenges for this resource-intensive application. In this paper, we present our work and lessons-learned on building an Image Processing Unit (IPU) for a satellite. We first investigate the performance of a variety of edge devices (comparing CPU, GPU, TPU, and VPU) for deep-learning-based image processing on satellites. Our goal is to identify devices that can achieve accurate results and are flexible when workload changes while satisfying the power and latency constraints of satellites. Our results demonstrate that hardware accelerators such as ASICs and GPUs are essential for meeting the latency requirements. However, state-of-the-art edge devices with GPUs may draw too much power for deployment on a satellite. Then, we use the findings gained from the performance analysis to guide the development of the IPU module for an upcoming satellite mission. We detail how to integrate such a module into an existing satellite architecture and the software necessary to support various missions utilizing this module.

翻译：卫星因其组件的尺寸和成本降低而变得更为普及。这导致规模较小的组织也有能力部署卫星，并在其上运行各种数据密集型应用。其中一项常见应用是图像分析，用于检测陆地、冰层、云层等，以实现地球观测。然而，卫星上部署的设备具有资源受限的特性，给这一资源密集型应用带来了额外挑战。在本文中，我们介绍了为卫星构建图像处理单元（IPU）的工作及经验教训。我们首先研究了多种边缘设备（比较CPU、GPU、TPU和VPU）在卫星上执行基于深度学习的图像处理时的性能。我们的目标是识别出能够在卫星的功耗和延迟约束下，实现准确结果并在工作负载变化时保持灵活性的设备。结果表明，ASIC和GPU等硬件加速器对于满足延迟要求至关重要。然而，配备GPU的最先进边缘设备在卫星上部署时可能功耗过高。接着，我们利用性能分析中获得的发现，指导即将开展的卫星任务中IPU模块的开发。我们详细说明了如何将此类模块集成到现有卫星架构中，以及为支持利用该模块的各种任务所需的软件。