RT Conference Proceedings
T1 Towards Very Low-Power Mobile Terminals through Optimized Computational Offloading
A1 Rexha, Hergys
A1 Lafond, Sébastien
A1 Rigazzi, Giovanni
A1 Kainulainen, Jani-Pekka
AB Energy consumption is a major issue for modern embedded mobile computing platforms, and with new technological developments, such as IoT and Edge/Fog computing, the number of connected embedded mobile computing systems israpidly increasing. Heterogeneous multi-core CPUs seek to improve the performance of these platforms, with a particular focus on energy efficiency. By using different techniques like DVFS, core mapping, and multi-threading, a substantial improvement in the achievable CPU energy efficiency level for Multi-processor system-on-chip (MPSoC) can be observed. However, controlling only the CPU power dissipation has a limited effect on the overall platform energy consumption. Other components of the platform, including memory, disk, and other peripherals, play an important role in the energy efficiency of the platform and need to be taken into account. The availability of different sleep strategies at various levels of the platform makes the energy efficiency issue even more complex. In this paper, we set the view of energy efficiency at the entire platform level and discuss computation offloading as a mechanism to help in reaching the optimal platform energy-efficient state. As an application, we consider object detection performed on several types of images to define when offloading is beneficial to the platform energy efficiency. We survey the energy efficiency of different neural network algorithms in an embedded environment, with the possibility to perform computation offloading, and discuss the obtained results concerning the level of object recognition accuracy provided by different neural networks.
PB IEEE
SN 978-1-7281-7440-2
YR 2020
FD 2020-07-21
LK https://hdl.handle.net/10016/30740
UL https://hdl.handle.net/10016/30740
LA eng
NO This paper has been presented at 2020 IEEE International Conference on Communications Workshops
NO This work has been partially funded by the H2020 EU/TW joint action 5G-DIVE (Grant no. 859881).
DS e-Archivo
RD 27 jul. 2024