Early-Exit Forecasting of Deep Neural Networks on Energy-Harvesting Edge Devices

Sixdenier PL, Deutel M, Wildermann S, Teich J (2026)

Publication Type: Conference contribution, Original article

Publication year: 2026

Publisher: Springer

Conference Proceedings Title: International Workshops of ECML PKDD 2026

Event location: Naples, Italy IT

Authors with CRIS profile

Pierre-Louis Sixdenier Lehrstuhl für Informatik 12 (Hardware-Software-Co-Design) Mark Deutel Lehrstuhl für Informatik 12 (Hardware-Software-Co-Design) Stefan Wildermann Lehrstuhl für Informatik 12 (Hardware-Software-Co-Design) Jürgen Teich Lehrstuhl für Informatik 12 (Hardware-Software-Co-Design)

How to cite

APA:

Sixdenier, P.-L., Deutel, M., Wildermann, S., & Teich, J. (2026). Early-Exit Forecasting of Deep Neural Networks on Energy-Harvesting Edge Devices. In International Workshops of ECML PKDD 2026. Naples, Italy, IT: Springer.

MLA:

Sixdenier, Pierre-Louis, et al. "Early-Exit Forecasting of Deep Neural Networks on Energy-Harvesting Edge Devices." Proceedings of the 7th International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM), Naples, Italy Springer, 2026.

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