计算机科学
量化(信号处理)
脑电图
现场可编程门阵列
联营
聚类分析
卷积神经网络
端到端原则
人工智能
修剪
卷积(计算机科学)
模式识别(心理学)
语音识别
人工神经网络
算法
嵌入式系统
心理学
精神科
农学
生物
作者
Chao Zhang,Zijian Tang,Taoming Guo,Jiaxin Lei,Jiaxin Xiao,Anhe Wang,Shuo Bai,Milin Zhang
标识
DOI:10.1109/iscas48785.2022.9937323
摘要
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.
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