@inproceedings{a87bae72adc94e06bbd84fcaa8d02788,
title = "A Customized Convolutional Neural Network Design Using Improved Softmax Layer for Real-time Human Emotion Recognition",
abstract = "This paper proposes an improved softmax layer algorithm and hardware implementation, which is applicable to an effective convolutional neural network of EEG-based real-time human emotion recognition. Compared with the general softmax layer, this hardware design adds threshold layers to accelerate the training speed and replace the Euler's base value with a dynamic base value to improve the network accuracy. This work also shows a hardware-friendly way to implement batch normalization layer on chip. Using the EEG emotion DEAP[7] database, the maximum and mean classification accuracy were achieved as 96.03% and 83.88% respectively. In this work, the usage of improved softmax layer can save up to 15% of training model convergence time and also increase by 3 to 5% the average accuracy.",
keywords = "Batch Normalization Layer, Convolutional Neural Network, Deep Learning, Hardware Machine Learning, Improved Softmax Layer, Threshold Layer",
author = "Wang, {Kai Yen} and Huang, {Yu De} and Ho, {Yun Lung} and Fang, {Wai Chi}",
year = "2019",
month = mar,
doi = "10.1109/AICAS.2019.8771616",
language = "English",
series = "Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "102--106",
booktitle = "Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019",
address = "United States",
note = "1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019 ; Conference date: 18-03-2019 Through 20-03-2019",
}