论文标题
Salenet：使用EEG进行持续注意水平评估的低功率端到端CNN加速器
SaleNet: A low-power end-to-end CNN accelerator for sustained attention level evaluation using EEG
论文作者
论文摘要
本文提出了Salenet-端到端卷积神经网络（CNN），用于使用前额叶脑电图（EEG）进行持续注意水平评估。提出了一种偏置驱动的修剪方法，以及组卷积，全局平均池（GAP），接近零的修剪，重量聚类和模型压缩的量化，达到总压缩比为183.11x。在这项工作中，压缩的分配器在记录的6个受试者EEG数据库中获得了最先进的主题持续注意力水平分类精度为84.2％。该沙发在ARTIX-7 FPGA上实施，竞争功耗为0.11 W，能源效率为8.19 GOPS/W。
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.
