Multicore embedded sensing system based on lightweight neural network

ABSTRACT

To the complexity of networks and the diversity of circuits, multicore embedded sensing systems suffer from low accuracy and efficiency in measuring temperature. To improve the measurement accuracy and efficiency of multicore embedded sensing systems, this paper utilised knowledge distillation, model pruning and parameter quantisation to lightweight neural networks. Meanwhile, the lightweight neural network was applied to multicore embedded sensing systems and the layout of multicore embedded sensing systems based on it was analysed from the perspectives of processor layout, storage design and link network, providing a reference and theoretical basis for further application of multicore embedded sensing systems.

KEYWORDS:

• Sensing system
• lightweight neural network
• multicore processors
• embedded systems
• linked networks

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All data generated or analysed during this study are included in this published article.

Additional information

Notes on contributors

Mingcai Zheng

Mingcai Zheng was born in Poyang, Jiangxi, P.R. China, in 1980. Associate professor. He received the Master degree from Jiangxi University of Finance and Economics, P.R. China. Now, he works in Network Engineering School of Jiangxi University of Software Professional Technology. His research interest include computational intelligence, information security and big data.