Toward Model Compression for a Deep Learning–Based Solar Flare Forecast on Satellites

Abstract Timely solar flare forecasting is challenged by the delay of transmitting vast amounts of data from the satellite to the ground. To avoid this delay, it is expected that forecasting models will be deployed on satellites. Thus, transmitting forecasting results instead of huge volumes of observation data would greatly save network bandwidth and reduce forecasting delay. However, deep-learning models have a huge number of parameters so they need large memory and strong computing power, which hinders their deployment on satellites with limited memory and computing resources. Therefore, there is a great need to compress forecasting models for efficient deployment on satellites. First, three typical compression methods, namely knowledge distillation, pruning, and quantization, are examined individually for compressing of solar flare forecasting models. And then, an assembled compression model is proposed for better compressing solar flare forecasting models. The experimental results demonstrate that the assembled compression model can compress a pretrained solar flare forecasting model to only 1.67% of its original size while maintaining forecasting accuracy.