Online continual streaming learning for embedded space applications

This paper proposes an online continual learning (OCL) methodology tested on hardware and validated for space applications using an object detection close-proximity operations task. The proposed OCL algorithm simulates a streaming scenario and uses experience replay to enable the model to update its knowledge without suffering catastrophic forgetting by saving past inputs in an onboard reservoir that will be sampled during updates. A stream buffer is introduced to enable online training, i.e., the ability to update the model as data is streamed, one sample at a time, rather than being available in batches. Hyperparameters such as buffer sizes, update rate, batch size, batch concatenation parameters and number of iterations per batch are all investigated to find an optimized approach for the incremental domain and streaming learning task. The algorithm is tested on a customized dataset for space applications simulating changes in visual environments that significantly impact the deployed model’s performance. Our OCL methodology uses Weighted Sampling, a novel approach which allows the system to analytically choose more useful input samples during training, the results show that a model can be updated online achieving up to 60% Average Learning while Average Forgetting can be as low as 13% all with a Model Size Efficiency of 1, meaning the model size does not increase. An additional contribution is an implementation of On-Device Continual Training for embedded applications, a hardware experiment is carried out on the Zynq 7100 FPGA where a pre-trained CNN model is updated online using our FPGA backpropagation pipeline and OCL methodology to take into account new data and satisfactorily complete the planned task in less than 5 min achieving 90 FPS.