AI Waste Prevention
Stefan C. Reif, Benedict Herzog, Judith Hemp, Wolfgang Schröder‐Preikschat, Timo Hönig · 2021
Artificial Intelligence (AI) has changed our daily lives. The evolution from centralised cloud-hosted services towards embedded and mobile devices has shifted the focus from quality-related aspects towards the resource demand of machine learning. Its pervasiveness demands for "green" AI---both the development and the operation of AI models still include significant resource investments in terms of processing time and power demand. In order to prevent such AI Waste, this paper presents Precious, an approach, as well as practical implementation, that estimates execution time and power draw of neural networks (NNs) that execute on a commercially-available off-the-shelf accelerator hardware (i.e., Google Coral Edge TPU). The evaluation of our implementations shows that Precious accurately estimates time and power demand.