Green incremental learning - Energy efficient ramp-up for AI-enhanced part recognition in reverse logistics

Artificial Intelligence (AI) has made significant progress in supporting circular economy and reverse logistics by learning from diverse data to predict, e.g., routes or to assist workers in sorting. However, it remains an open question how AI can be integrated and trained into such operational processes, where little to no data has been collected previously. Traditionally, AI models would only be rated by their accuracy. This paper aims to introduce the concept of green incremental learning, i.e. rating AI models not only for their accuracy but to evaluate energy efficiency as well. A ramp-up of a data-driven AI system for part recognition is explored under consideration of energy efficiency. Therefore, we combine online and incremental learning, working with growing data sets to simulate a ramp-up phase. We present experiments of incremental learning on business and image data, partially supported by regular joint training steps. We start local CPU-based machine learning and prediction on business data from the first sample. Finally, we compare incremental learning to traditional batch learning and show energy-saving potential of up to 62 % without a significant drop in accuracy.