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AI Waste Prevention: Time and Power Estimation for Edge Tensor Processing Units: Poster

Publication by Stefan Reif, Benedict Herzog, Judith Hemp, Wolfgang Schröder-Preikschat, Timo Hönig
Related to the Energy-, Latency- And Resilience-aware Networking (e.LARN) project
Published in e-Energy '21: Proceedings of the Twelfth ACM International Conference on Future Energy Systems, 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.