This article has been written by Dr. Rajan Bedi, the CEO and founder of Spacechips.
Satellite operators are increasingly acquiring more and more data in-orbit and would prefer to process this on-board the payload to extract value-added insights rather than downlink huge amounts of information to a cloud for post-processing on the ground. Limitations with existing space-grade semiconductor technology and/or RF bandwidth constraints have hindered the amount of data that can be processed in real-time. I know of several customers who have had to descope their mission aspirations because of both reasons as their downlink needs would have violated ITU regulations.
In contrast, localised processing as close to the originating data source as possible, i.e. at the Edge, is based on real-time computation of large amounts of information from multiple sensors, acquired using low-latency, deterministic interfaces, in a small, low-power form factor with unique thermal and reliability requirements. Extracting the analytics in-orbit significantly reduces delay and the RF downlink bandwidth – we are effectively moving the data centre to the origin of the raw data!
In this post, I want to discuss and compare microprocessors and FPGAs for intensive on-board processing at the Edge. Some applications ingest huge amounts of data from multiple sensors with different bandwidths, e.g. RF, LIDAR, imaging and GNSS, and require critical decisions to be made in real-time, e.g. recognition and classification of objects for spacecraft situational awareness, i.e. identification of friend or foe, space-debris collision avoidance, high-definition video Earth observation and space-exploration in-situ, resource utilisation. There is also an increasing trend for autonomous on-board processing using machine-learning techniques to extract analytics in-orbit.
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