Reaching the Edge of the Edge: Image Analysis in Space
This work addresses the problem of enabling efficient image processing for Earth observation on satellites, which is crucial for smaller organizations deploying satellites, but it is incremental as it builds on existing edge device evaluations and integration methods.
The paper tackles the challenge of performing deep-learning-based image analysis on resource-constrained satellites by evaluating edge devices like CPUs, GPUs, TPUs, and VPUs, finding that hardware accelerators are essential for latency but may draw too much power, and uses these insights to develop an Image Processing Unit for an upcoming satellite mission.
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.