EExNAS: Early-Exit Neural Architecture Search Solutions for Low-Power Wearable Devices
Author:
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9502425/9502469/09502503.pdf?arnumber=9502503
Cited by 14 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Evolutionary Neural Architecture Search and Its Applications in Healthcare;Computer Modeling in Engineering & Sciences;2024
2. LoCoExNet: Low-Cost Early Exit Network for Energy Efficient CNN Accelerator Design;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2023-12
3. Lightweight accurate trigger to reduce power consumption in sensor-based continuous human activity recognition;Pervasive and Mobile Computing;2023-12
4. PELSI: Power-Efficient Layer-Switched Inference;2023 IEEE 29th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA);2023-08-30
5. Stress Detection Using Context-Aware Sensor Fusion From Wearable Devices;IEEE Internet of Things Journal;2023-08-15
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