Contributions to Proceedings:
S. Holly, A. Wendt, M. Lechner:
"Profiling Energy Consumption of Deep Neural Networks on NVIDIA Jetson Nano";
in: "2020 11th International Green and Sustainable Computing Workshops (IGSC)",
issued by: IEEE Explore;
IEEE Xplore,
USA,
2020,
ISBN: 978-0-7381-4307-1,
1
- 6.
English abstract:
Improving the capabilities of embedded devices and accelerators for Deep Neural Networks (DNN) leads to a shift from cloud to edge computing. Especially for battery-powered systems, intelligent energy management is critical. In this work, we provide a measurement base for power estimation on NVIDIA Jetson devices. We analyze the effects of different CPU and GPU settings on power consumption, latency, and energy for complete DNNs as well as for individual layers. Furthermore, we provide optimal settings for minimal power and energy consumption for an NVIDIA Jetson Nano.
Keywords:
Power measurement;Sensors;Graphics processing units;Power demand;Neural networks;Hardware;Energy consumption;power;energy;latency;NVIDIA;Jetson;deep neural network;profiling
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/IGSC51522.2020.9290876
Electronic version of the publication:
https://publik.tuwien.ac.at/files/publik_293778.pdf
Created from the Publication Database of the Vienna University of Technology.