scholarly journals Memristor crossbar architectures for implementing deep neural networks

2021 ◽  
Author(s):  
Xiaoyang Liu ◽  
Zhigang Zeng
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Neural Networks ◽  
Activation Functions ◽  
Simulation Results ◽  
Memristor Crossbars ◽  
Memristor Crossbar ◽  
Fully Connected

AbstractThe paper presents memristor crossbar architectures for implementing layers in deep neural networks, including the fully connected layer, the convolutional layer, and the pooling layer. The crossbars achieve positive and negative weight values and approximately realize various nonlinear activation functions. Then the layers constructed by the crossbars are adopted to build the memristor-based multi-layer neural network (MMNN) and the memristor-based convolutional neural network (MCNN). Two kinds of in-situ weight update schemes, which are the fixed-voltage update and the approximately linear update, respectively, are used to train the networks. Consider variations resulted from the inherent characteristics of memristors and the errors of programming voltages, the robustness of MMNN and MCNN to these variations is analyzed. The simulation results on standard datasets show that deep neural networks (DNNs) built by the memristor crossbars work satisfactorily in pattern recognition tasks and have certain robustness to memristor variations.

scholarly journals Binary and Multiclass Text Classification by Means of Separable Convolutional Neural Network

Inventions ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 70
Author(s):  
Elena Solovyeva ◽  
Ali Abdullah
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Recurrent Neural Networks ◽  
Low Cost ◽  
Computational Cost ◽  
High Accuracy ◽  
Activation Functions ◽  
Fully Connected ◽  
Fully Connected Networks

In this paper, the structure of a separable convolutional neural network that consists of an embedding layer, separable convolutional layers, convolutional layer and global average pooling is represented for binary and multiclass text classifications. The advantage of the proposed structure is the absence of multiple fully connected layers, which is used to increase the classification accuracy but raises the computational cost. The combination of low-cost separable convolutional layers and a convolutional layer is proposed to gain high accuracy and, simultaneously, to reduce the complexity of neural classifiers. Advantages are demonstrated at binary and multiclass classifications of written texts by means of the proposed networks under the sigmoid and Softmax activation functions in convolutional layer. At binary and multiclass classifications, the accuracy obtained by separable convolutional neural networks is higher in comparison with some investigated types of recurrent neural networks and fully connected networks.

scholarly journals A HYBRID MODEL USING THE PRETRAINED BERT AND DEEP NEURAL NETWORKS WITH RICH FEATURE FOR EXTRACTIVE TEXT SUMMARIZATION

2021 ◽  
Vol 37 (2) ◽  
pp. 123-143
Author(s):  
Tuan Minh Luu ◽  
Huong Thanh Le ◽  
Tan Minh Hoang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Text Summarization ◽  
Training Dataset ◽  
Extractive Summarization ◽  
Input Text ◽  
Summarization System ◽  
Fully Connected

Deep neural networks have been applied successfully to extractive text summarization tasks with the accompany of large training datasets. However, when the training dataset is not large enough, these models reveal certain limitations that affect the quality of the system’s summary. In this paper, we propose an extractive summarization system basing on a Convolutional Neural Network and a Fully Connected network for sentence selection. The pretrained BERT multilingual model is used to generate embeddings vectors from the input text. These vectors are combined with TF-IDF values to produce the input of the text summarization system. Redundant sentences from the output summary are eliminated by the Maximal Marginal Relevance method. Our system is evaluated with both English and Vietnamese languages using CNN and Baomoi datasets, respectively. Experimental results show that our system achieves better results comparing to existing works using the same dataset. It confirms that our approach can be effectively applied to summarize both English and Vietnamese languages.

scholarly journals Notes on the Symmetries of 2-Layer ReLU-Networks

2020 ◽  
Vol 1 ◽  
pp. 6
Author(s):  
Henning Petzka ◽  
Martin Trimmel ◽  
Cristian Sminchisescu
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Complete Characterization ◽  
Activation Functions ◽  
Network Function ◽  
Fully Connected ◽  
Fully Connected Networks

Symmetries in neural networks allow different weight configurations leading to the same network function. For odd activation functions, the set of transformations mapping between such configurations have been studied extensively, but less is known for neural networks with ReLU activation functions. We give a complete characterization for fully-connected networks with two layers. Apart from two well-known transformations, only degenerated situations allow additional transformations that leave the network function unchanged. Reduction steps can remove only part of the degenerated cases. Finally, we present a non-degenerate situation for deep neural networks leading to new transformations leaving the network function intact.

scholarly journals A Novel Architecture to Classify Histopathology Images Using Convolutional Neural Networks

2020 ◽  
Vol 10 (8) ◽  
pp. 2929 ◽  
Author(s):  
Ibrahem Kandel ◽  
Mauro Castelli
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
State Of The Art ◽  
Treatment Plan ◽  
Tissue Structure ◽  
Activation Functions ◽  
Proposed Model ◽  
Histopathology Images ◽  
Fully Connected

Histopathology is the study of tissue structure under the microscope to determine if the cells are normal or abnormal. Histopathology is a very important exam that is used to determine the patients’ treatment plan. The classification of histopathology images is very difficult to even an experienced pathologist, and a second opinion is often needed. Convolutional neural network (CNN), a particular type of deep learning architecture, obtained outstanding results in computer vision tasks like image classification. In this paper, we propose a novel CNN architecture to classify histopathology images. The proposed model consists of 15 convolution layers and two fully connected layers. A comparison between different activation functions was performed to detect the most efficient one, taking into account two different optimizers. To train and evaluate the proposed model, the publicly available PatchCamelyon dataset was used. The dataset consists of 220,000 annotated images for training and 57,000 unannotated images for testing. The proposed model achieved higher performance compared to the state-of-the-art architectures with an AUC of 95.46%.

scholarly journals Quantization of Deep Neural Networks for Accurate Edge Computing

2021 ◽  
Vol 17 (4) ◽  
pp. 1-11
Author(s):  
Wentao Chen ◽  
Hailong Qiu ◽  
Jian Zhuang ◽  
Chutong Zhang ◽  
Yu Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Segmentation ◽  
Automatic Speech Recognition ◽  
Deep Neural Networks ◽  
Biomedical Image ◽  
Wide Range ◽  
Improve Accuracy ◽  
Memory Reduction ◽  
Fully Connected

Deep neural networks have demonstrated their great potential in recent years, exceeding the performance of human experts in a wide range of applications. Due to their large sizes, however, compression techniques such as weight quantization and pruning are usually applied before they can be accommodated on the edge. It is generally believed that quantization leads to performance degradation, and plenty of existing works have explored quantization strategies aiming at minimum accuracy loss. In this paper, we argue that quantization, which essentially imposes regularization on weight representations, can sometimes help to improve accuracy. We conduct comprehensive experiments on three widely used applications: fully connected network for biomedical image segmentation, convolutional neural network for image classification on ImageNet, and recurrent neural network for automatic speech recognition, and experimental results show that quantization can improve the accuracy by 1%, 1.95%, 4.23% on the three applications respectively with 3.5x-6.4x memory reduction.

scholarly journals FORMING EVOLUTIONARY DESIGN OF NEURAL NETWORKS WITH DIFFERENT NODES

2014 ◽  
pp. 16-23
Author(s):  
Eva Volna
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Feedforward Neural Network ◽  
Evolutionary Design ◽  
Pattern Recognition Problem ◽  
Multilayer Feedforward Neural Network ◽  
Artificial Neural ◽  
Fully Connected

Evolution in artificial neural networks (e.g. neuroevolution) searches through the space of behaviours for a network that performs well at a given task. Here is presented a neuroevolution system evolving populations of neurons that are combined to form the fully connected multilayer feedforward neural network with fixed architecture. In this article, the transfer function has been shown to be an important part of architecture of the artificial neural network and have significant impact on an artificial neural network’s performance. In order to test the efficiency of described method, we applied it to the pattern recognition problem and to the alphabet coding problem.

scholarly journals Overview of Configuring Adaptive Activation Functions for Deep Neural Networks - A Comparative Study

2021 ◽  
Vol 3 (1) ◽  
pp. 10-22
Author(s):  
Wang Haoxiang ◽  
Smys S
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Process ◽  
Deep Neural Networks ◽  
Research Work ◽  
Nonlinear Function ◽  
Activation Function ◽  
Classification Error ◽  
Activation Functions ◽  
Research Article

Recently, the deep neural networks (DNN) have demonstrated many performances in the pattern recognition paradigm. The research studies on DNN include depth layer networks, filters, training and testing datasets. Deep neural network is providing many solutions for nonlinear partial differential equations (PDE). This research article comprises of many activation functions for each neuron. Besides, these activation networks are allowing many neurons within the neuron networks. In this network, the multitude of the functions will be selected between node by node to minimize the classification error. This is the reason for selecting the adaptive activation function for deep neural networks. Therefore, the activation functions are adapted with every neuron on the network, which is used to reduce the classification error during the process. This research article discusses the scaling factor for activation function that provides better optimization for the process in the dynamic changes of procedure. The proposed adaptive activation function has better learning capability than fixed activation function in any neural network. The research articles compare the convergence rate, early training function, and accuracy between existing methods. Besides, this research work provides improvements in debt ideas of the learning process of various neural networks. This learning process works and tests the solution available in the domain of various frequency bands. In addition to that, both forward and inverse problems of the parameters in the overriding equation will be identified. The proposed method is very simple architecture and efficiency, robustness, and accuracy will be high when considering the nonlinear function. The overall classification performance will be improved in the resulting networks, which have been trained with common datasets. The proposed work is compared with the recent findings in neuroscience research and proved better performance.

Deep distributed convolutional neural networks: Universality

2018 ◽  
Vol 16 (06) ◽  
pp. 895-919 ◽  
Author(s):  
Ding-Xuan Zhou
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Mathematical Foundation ◽  
Deep Convolutional Neural Networks ◽  
The Family ◽  
Fully Connected

Deep learning based on structured deep neural networks has provided powerful applications in various fields. The structures imposed on the deep neural networks are crucial, which makes deep learning essentially different from classical schemes based on fully connected neural networks. One of the commonly used deep neural network structures is generated by convolutions. The produced deep learning algorithms form the family of deep convolutional neural networks. Despite of their power in some practical domains, little is known about the mathematical foundation of deep convolutional neural networks such as universality of approximation. In this paper, we propose a family of new structured deep neural networks: deep distributed convolutional neural networks. We show that these deep neural networks have the same order of computational complexity as the deep convolutional neural networks, and we prove their universality of approximation. Some ideas of our analysis are from ridge approximation, wavelets, and learning theory.

scholarly journals Using Summary Layers to Probe Neural Network Behaviour

2020 ◽  
Vol 32 (2) ◽  
Author(s):  
Marelie Hattingh Davel
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Network Architectures ◽  
Feedforward Networks ◽  
Empirical Results ◽  
Analysis Process ◽  
Generalisation Ability ◽  
Neural Network Architectures ◽  
Fully Connected

No framework exists that can explain and predict the generalisation ability of deep neural networks in general circumstances. In fact, this question has not been answered for some of the least complicated of neural network architectures: fully-connected feedforward networks with rectified linear activations and a limited number of hidden layers. For such an architecture, we show how adding a summary layer to the network makes it more amenable to analysis, and allows us to define the conditions that are required to guarantee that a set of samples will all be classified correctly. This process does not describe the generalisation behaviour of these networks, but produces a number of metrics that are useful for probing their learning and generalisation behaviour. We support the analytical conclusions with empirical results, both to confirm that the mathematical guarantees hold in practice, and to demonstrate the use of the analysis process.

Sign in / Sign up

Export Citation Format

Share Document