Reaching the Edge of the Edge: Image Analysis in Space

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 this satellite. We first highlight the resource constraints based on a deployed satellite performing machine learning on satellite imagery in orbit, including the required latency, power budget, and the network bandwidth limitations driving the need for such a solution. We then 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 are flexible when the 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.