Generic model implementation of deep neural network activation functions using GWO-optimized SCPWL model on FPGA

2020 ◽  
Vol 77 ◽  
pp. 103141 ◽  
Author(s):  
Hussein M.H. Al-Rikabi ◽  
Mohannad A.M. Al-Ja’afari ◽  
Ameer H. Ali ◽  
Saif H. Abdulwahed
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Generic Model ◽  
Activation Functions ◽  
Network Activation

Enabling Resistive-RAM-based Activation Functions for Deep Neural Network Acceleration

2020 ◽  
Author(s):  
Zihan Zhang ◽  
Taozhong Li ◽  
Ning Guan ◽  
Qin Wang ◽  
Guanghui He ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Activation Functions ◽  
Resistive Ram

Neural Network Activation Functions with Electro-Optic Absorption Modulators

2018 ◽  
Author(s):  
Jonathan George ◽  
Armin Mehrabian ◽  
Rubab Amin ◽  
Paul R. Prucnal ◽  
Tarek El-Ghazawi ◽  
...  
Keyword(s):  
Neural Network ◽  
Activation Functions ◽  
Network Activation ◽  
Electro Optic

scholarly journals An Analysis of State-of-the-art Activation Functions For Supervised Deep Neural Network

2021 ◽  
Author(s):  
Anh Nguyen ◽  
Khoa Pham ◽  
Dat Ngo ◽  
Thanh Ngo ◽  
Lam Pham
Keyword(s):  
Neural Network ◽  
Supervised Classification ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Network Architectures ◽  
Activation Functions ◽  
Scene Classification ◽  
Learning Network ◽  
Deep Learning Network

This paper provides an analysis of state-of-the-art activation functions with respect to supervised classification of deep neural network. These activation functions comprise of Rectified Linear Units (ReLU), Exponential Linear Unit (ELU), Scaled Exponential Linear Unit (SELU), Gaussian Error Linear Unit (GELU), and the Inverse Square Root Linear Unit (ISRLU). To evaluate, experiments over two deep learning network architectures integrating these activation functions are conducted. The first model, basing on Multilayer Perceptron (MLP), is evaluated with MNIST dataset to perform these activation functions.Meanwhile, the second model, likely VGGish-based architecture, is applied for Acoustic Scene Classification (ASC) Task 1A in DCASE 2018 challenge, thus evaluate whether these activation functions work well in different datasets as well as different network architectures.

CTL-DNNet: Effective Circular Traffic Light Recognition with a Deep Neural Network

2017 ◽  
Vol 31 (11) ◽  
pp. 1750037 ◽  
Author(s):  
Di Wang ◽  
Hong Bao ◽  
Feifei Zhang
Keyword(s):  
Neural Network ◽  
Autonomous Vehicles ◽  
Deep Neural Network ◽  
Road Traffic ◽  
High Accuracy ◽  
Activation Functions ◽  
Traffic Light ◽  
Traffic Lights ◽  
Learning Network ◽  
Deep Learning Network

This paper proposed an algorithm for a deep learning network for identifying circular traffic lights (CTL-DNNet). The sample labeling process uses translation to increase the number of positive samples, and the similarity is calculated to reduce the number of negative samples, thereby reducing overfitting. We use a dataset of approximately 370[Formula: see text]000 samples, with approximately 20[Formula: see text]000 positive samples and approximately 350[Formula: see text]000 negative samples. The datasets are generated from images taken at the Beijing Garden Expo. To obtain a very robust method for the detection of traffic lights, we use different layers, different cost functions and different activation functions of the depth neural network for training and comparison. Our algorithm has evaluated autonomous vehicles in varying illumination and gets the result with high accuracy and robustness. The experimental results show that CTL-DNNet is effective at recognizing road traffic lights in the Beijing Garden Expo area.

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