Speaker
Description
The number of CubeSats launched for data-intensive applications is increasing due to the modularity and reduced cost these platforms provide. Consequently, there is a growing need for efficient data processing and compression. Tailoring onboard processing with Machine Learning to specific mission tasks can optimise downlink usage by focusing only on relevant data, ultimately reducing the required bandwidth. The Edge SpAIce project showcases onboard data filtering and reduction by using Machine Learning to identify plastic litter in the oceans. The deployment pipeline, including drastic model compression and deployment using the open-source hls4ml and QONNX tools, enables high-performance, low-power, low-cost computation on onboard FPGA processors. We present lab-based demonstration results, highlighting performance in terms of accuracy, throughput, and power consumption, and discuss planned deployment aspects.
