用2D CNN替代1D CNN，实现在边缘设备上高效去伪迹
E2CAR: An Efficient 2D-CNN Framework for Real-Time EEG Artifact Removal on Edge Devices
- 用2D CNN替代传统1D CNN，降低计算开销
- 在TPU上实现90%推理时间减少，功耗降18.98%
- 适合资源受限的实时脑电信号处理场景
脑电图（EEG）信号常受伪迹干扰，影响后续分析精度。传统去伪迹方法计算量大，难以在边缘设备实现实时处理。本文提出一种新方法：将一维卷积神经网络（1-D CNN）替换为二维卷积神经网络（2-D CNN），并部署于边缘张量处理单元（Edge TPU）——一种广泛用于边缘设备的开源低延迟、低功耗硬件加速器。基于此构建的高效2D-CNN去伪迹框架（E2CAR），在边缘TPU上实现90%的推理时间缩减，功耗降低18.98%，同时保持与现有方法相当的去伪迹性能。该方案推动了边缘设备上的高效脑电信号处理。
原文摘要 · Abstract (English)
Electroencephalography (EEG) signals are frequently contaminated by artifacts, affecting the accuracy of subsequent analysis. Traditional artifact removal methods are often computationally expensive and inefficient for real-time applications in edge devices. This paper presents a method to reduce the computational cost of most existing convolutional neural networks (CNN) by replacing one-dimensional (1-D) CNNs with two-dimensional (2-D) CNNs and deploys them on Edge Tensor Processing Unit (TPU), which is an open-resource hardware accelerator widely used in edge devices for low-latency, low-power operation. A new Efficient 2D-CNN Artifact Removal (E2CAR) framework is also represented using the method above, and it achieves a 90\% reduction in inference time on the TPU and decreases power consumption by 18.98\%, while maintaining comparable artifact removal performance to existing methods. This approach facilitates efficient EEG signal processing on edge devices.
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