Designing Multi-Class Classifiers for Sub-mA Microcontroller Platforms

Qi Hui Sun, Mei Lan Lin, Chia Heng Tu

研究成果: Conference contribution

摘要

With the prevalence of deep learning technology in various application domains, a recent trend is to deploy convolutional neural networks onto the sensors in the field enabling new smart applications, such as poaching detection, discovering endanger species, and estimating wildlife populations in environmental monitoring. Among the ultra-low-power hardware platforms with microcontrollers for the computations, the sub-mA platforms (i.e., lower-end ultra-low-power platforms) would be desired for monitoring in the field since they can achieve a longer lifetime given a fixed amount of power budget provided by the batteries. In this work, we propose a new approach to develop convolutional neural networks to run on such sub-mA platforms, usually with less than 10 KB memory for keeping the runtime data. The new approach combines the concepts of the One-vs-All (OVA) strategy and the transfer learning technology to build networks with small memory footprints. Our experimental results show our approach achieves the accuracy of 77.0% of a four-class classification problem for a poaching detection application as fast as 1.1 seconds.

原文English
主出版物標題2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面131-132
頁數2
ISBN(電子)9798350324174
DOIs
出版狀態Published - 2023
事件2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Pingtung, Taiwan
持續時間: 2023 7月 172023 7月 19

出版系列

名字2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings

Conference

Conference2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023
國家/地區Taiwan
城市Pingtung
期間23-07-1723-07-19

All Science Journal Classification (ASJC) codes

  • 人工智慧
  • 人機介面
  • 資訊系統
  • 資訊系統與管理
  • 電氣與電子工程
  • 媒體技術
  • 儀器

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