An Energy-efficient and Programmable RISC-V CNN Coprocessor for Real-time Epilepsy Detection and Identification on Wearable Devices

Yi Wen Hung, Yao Tse Chang, Shuenn Yuh Lee, Chou Ching Lin, Gia Shing Shieh

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

This paper has proposed an energy-efficient epilepsy detection framework for embedded systems. The epilepsy detection framework is implemented in 11 layers Convolution Neural Network (CNN) with a 2-stage RISC-V core and a coprocessor to accelerate CNN inferences. The CNN algorithm provides 97.8% and 93.5% accuracy on floating-point and fixedpoint operations respectively. The proposed CNN coprocessor is designed to offload CNN inference from RISC-V core to hardware with 51 nJ data transfer energy and 0.9 μJ inference energy for each 500 points input data frame. The coprocessor reduces the runtime of CNN inferences over 106x to perform only 0.012 s latency for each classification. According to the energy-efficient coprocessor, an AI-based solution is practical for real-time epilepsy detection on wearable devices for consumer electronics.

原文English
主出版物標題2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781665433280
DOIs
出版狀態Published - 2021
事件8th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021 - Penghu, Taiwan
持續時間: 2021 9月 152021 9月 17

出版系列

名字2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021

Conference

Conference8th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021
國家/地區Taiwan
城市Penghu
期間21-09-1521-09-17

All Science Journal Classification (ASJC) codes

  • 人工智慧
  • 電腦科學應用
  • 電氣與電子工程
  • 控制和優化
  • 儀器

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