We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Implementation of Multi-Exit Neural-Network Inferences for an Image-Based Sensing System with Energy Harvesting.
- Authors
Li, Yuyang; Gao, Yuxin; Shao, Minghe; Tonecha, Joseph T.; Wu, Yawen; Hu, Jingtong; Lee, Inhee
- Abstract
Wireless sensor systems powered by batteries are widely used in a variety of applications. For applications with space limitation, their size was reduced, limiting battery energy capacity and memory storage size. A multi-exit neural network enables to overcome these limitations by filtering out data without objects of interest, thereby avoiding computing the entire neural network. This paper proposes to implement a multi-exit convolutional neural network on the ESP32-CAM embedded platform as an image-sensing system with an energy constraint. The multi-exit design saves energy by 42.7% compared with the single-exit condition. A simulation result, based on an exemplary natural outdoor light profile and measured energy consumption of the proposed system, shows that the system can sustain its operation with a 3.2 kJ (275 mAh @ 3.2 V) battery by scarifying the accuracy only by 2.7%.
- Subjects
ENERGY harvesting; CONVOLUTIONAL neural networks; ENERGY consumption
- Publication
Journal of Low Power Electronics & Applications, 2021, Vol 11, Issue 3, p34
- ISSN
2079-9268
- Publication type
Article
- DOI
10.3390/jlpea11030034