Reaching the Edge of the Edge: Image Analysis in Space
Robert Bayer (1), Julian Priest (1), P{\i}nar T\"oz\"un (1) ((1) IT, University of Copenhagen)

TL;DR
This paper evaluates various edge devices for satellite image analysis, identifying suitable hardware accelerators, and details the development and integration of an Image Processing Unit for a satellite mission.
Contribution
It provides a comprehensive performance comparison of edge devices and guides the design of an efficient IPU for resource-constrained satellites.
Findings
GPU and ASIC accelerators meet latency needs
High-power GPUs are unsuitable for satellite deployment
Performance analysis guides IPU development
Abstract
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…
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Taxonomy
TopicsSpacecraft Design and Technology · Satellite Communication Systems · Age of Information Optimization
