.png){kind=icon}
返回
Enabling Lightweight Device-Free Wireless Sensing With Network Pruning and Quantization
.png){kind=icon}
PRE
AI
DOI：10.1109/JSEN.2021.3129466.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Deep learning based device-free wireless sensing systems have achieved satisfactory performance in sensing human gesture, identity, location, etc.. However, subject to the limited computation and storage resources of wireless devices, complex deep learning algorithms could not run on these devices, which limits the practical implementation. In this paper, motivated by the emerging edge intelligence technique, we try to explore and exploit realizing lightweight device-free wireless sensing with network pruning and network quantization methods, which could reduce the complexity of a sensing system while keeping its sensing performance almost unchanged. Specifically, we propose a Taylor criterion ranking based network pruning strategy to remove the nonessential neurons so as to reduce the computational complexity and storage requirement, and design a network quantization strategy to quantize network parameters so as to further reduce the storage requirement. We design a mmWave-based device-free gesture recognition testbed to evaluate the proposed strategies. Extensive experimental results show that the developed strategies reduce the computational complexity and storage requirement to 27% and 14%, respectively, with the recognition accuracy reduced by only 2%.
Keyword:
Wireless sensor networks
Sensors
Wireless communication
Performance evaluation
Quantization (signal)
Robot sensing systems
Deep learning
Device-free
wireless sensing
gesture recognition
deep learning
期刊
IF:
4.5
论文数:
2.2W
被引数:
7.3W
机构
引用论文
Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing边缘智能: 用边缘计算铺平人工智能的最后一英里.png)
PROCEEDINGS OF THE IEEE
OAAI
PROCEEDINGS OF THE IEEEIF25.9
OAAI分享收藏Learning to Sense: Deep Learning for Wireless Sensing with Less Training Efforts学习感知: 以更少的训练努力实现无线传感的深度学习
PREAI
分享收藏Effect of acidosis and uninepherectomy on isozymes of adenylosuccinate synthetase in rat kidney
PREAI
分享收藏
查看更多内容