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

by   Chao Zhang, et al.
Tsinghua University

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.


On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks

In this paper, we propose a novel Convolutional Neural Network (CNN) app...

EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery Classification

Classification of EEG-based motor imagery (MI) is a crucial non-invasive...

Kernel Quantization for Efficient Network Compression

This paper presents a novel network compression framework Kernel Quantiz...

Neonatal EEG Interpretation and Decision Support Framework for Mobile Platforms

This paper proposes and implements an intuitive and pervasive solution f...

Corticomorphic Hybrid CNN-SNN Architecture for EEG-based Low-footprint Low-latency Auditory Attention Detection

In a multi-speaker "cocktail party" scenario, a listener can selectively...

Arrhythmia Classifier Using Convolutional Neural Network with Adaptive Loss-aware Multi-bit Networks Quantization

Cardiovascular disease (CVDs) is one of the universal deadly diseases, a...

Multi-modal Sentiment Analysis using Super Characters Method on Low-power CNN Accelerator Device

Recent years NLP research has witnessed the record-breaking accuracy imp...

Please sign up or login with your details

Forgot password? Click here to reset