Resource-demand Estimation for Edge Tensor Processing Units

Herzog B, Reif S, Hemp J, Hoenig T, Schröder-Preikschat W (2022)

Publication Type: Journal article

Publication year: 2022

Journal

ACM Transactions on Embedded Computing Systems Association for Computing Machinery (ACM)

Book Volume: 21

Journal Issue: 5

DOI: 10.1145/3520132

Abstract

Machine learning has shown tremendous success in a large variety of applications. The evolution of machine-learning applications from cloud-based systems to mobile and embedded devices has shifted the focus from only quality-related aspects towards the resource demand of machine learning. For embedded systems, dedicated accelerator hardware promises the energy-efficient execution of neural network inferences. Their precise resource demand in terms of execution time and power demand, however, is undocumented. Developers, therefore, face the challenge to fine-tune their neural networks such that their resource demand matches the available budgets.

Authors with CRIS profile

Stefan Reif Lehrstuhl für Informatik 4 (Verteilte Systeme und Betriebssysteme) Judith Hemp Sonderforschungsbereich/Transregio 89 Invasives Rechnen Wolfgang Schröder-Preikschat Lehrstuhl für Informatik 4 (Verteilte Systeme und Betriebssysteme)

Involved external institutions

Ruhr-Universität Bochum (RUB)

Germany (DE)

How to cite

APA:

Herzog, B., Reif, S., Hemp, J., Hoenig, T., & Schröder-Preikschat, W. (2022). Resource-demand Estimation for Edge Tensor Processing Units. ACM Transactions on Embedded Computing Systems, 21(5). https://doi.org/10.1145/3520132

MLA:

Herzog, Benedict, et al. "Resource-demand Estimation for Edge Tensor Processing Units." ACM Transactions on Embedded Computing Systems 21.5 (2022).

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