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

Reif S, Herzog B, Hemp J, Schröder-Preikschat W, Hönig T (2021)

Publication Type: Conference contribution

Publication year: 2021

Publisher: Association for Computing Machinery, Inc

Pages Range: 300-301

Conference Proceedings Title: e-Energy 2021 - Proceedings of the 2021 12th ACM International Conference on Future Energy Systems

Event location: Online IT

ISBN: 9781450383332

DOI: 10.1145/3447555.3466579

Abstract

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.

Authors with CRIS profile

Stefan Reif Lehrstuhl für Informatik 4 (Verteilte Systeme und Betriebssysteme) Benedict Herzog Lehrstuhl für Informatik 4 (Verteilte Systeme und Betriebssysteme) Wolfgang Schröder-Preikschat Lehrstuhl für Informatik 4 (Verteilte Systeme und Betriebssysteme)

Involved external institutions

Ruhr-Universität Bochum (RUB) DE Germany (DE)

How to cite

APA:

Reif, S., Herzog, B., Hemp, J., Schröder-Preikschat, W., & Hönig, T. (2021). AI Waste Prevention: Time and Power Estimation for Edge Tensor Processing Units: Poster. In e-Energy 2021 - Proceedings of the 2021 12th ACM International Conference on Future Energy Systems (pp. 300-301). Online, IT: Association for Computing Machinery, Inc.

MLA:

Reif, Stefan, et al. "AI Waste Prevention: Time and Power Estimation for Edge Tensor Processing Units: Poster." Proceedings of the 12th ACM International Conference on Future Energy Systems, e-Energy 2021, Online Association for Computing Machinery, Inc, 2021. 300-301.

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