SaleNet: A low-power end-to-end CNN accelerator for sustained attention level evaluation using EEG

This paper proposes SaleNet - an end-to-end convolutional neural network (CNN) for sustained attention level evaluation using prefrontal electroencephalogram (EEG). A bias-driven pruning method is proposed together with group convolution, global average pooling (GAP), near-zero pruning, weight clust...

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Bibliographic Details
Published inIEEE International Symposium on Circuits and Systems proceedings pp. 2304 - 2308
Main Authors Zhang, Chao, Tang, Zijian, Guo, Taoming, Lei, Jiaxin, Xiao, Jiaxin, Wang, Anhe, Bai, Shuo, Zhang, Milin
Format Conference Proceeding
LanguageEnglish
Published IEEE 28.05.2022
Subjects
Brain-machine interface (BMI)
Convolution
Convolutional neural network (CNN)
Electroencephalography
Field-programmable gate array (FPGA)
Logic gates
Model compression
Neural networks
Power demand
Quantization (signal)
Sustained attention level evaluation
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ISSN2158-1525
DOI10.1109/ISCAS48785.2022.9937323

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Summary:This paper proposes SaleNet - an end-to-end convolutional neural network (CNN) for sustained attention level evaluation using prefrontal electroencephalogram (EEG). A bias-driven pruning method is proposed together with group convolution, global average pooling (GAP), near-zero pruning, weight clustering and quantization for the model compression, achieving a total compression ratio of 183. 11x. The compressed SaleNet obtains a state-of-the-art subject-independent sustained attention level classification accuracy of 84.2% on the recorded 6-subject EEG database in this work. The SaleNet is implemented on a Artix-7 FPGA with a competitive power consumption of 0.11 W and an energy-efficiency of 8.19 GOps/w.
ISSN:2158-1525
DOI:10.1109/ISCAS48785.2022.9937323