Comparison of Performance of Parallel Computation of CPU Cores on CNN model

Datta, Debajit; Mittal, Dhruv; Mathew, Nirmal Philipose; Sairabanu, J.

doi:10.1109/ic-etite47903.2020.142

Cited by 10 publications

(8 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was developed by John F. Canny [ 27 ] in 1986. Blurring filter [ 27 , 28 ] is a low pass filter, because it allows low frequency to enter and stop high frequency. Here, frequency means the change of pixel value.…”

Section: Proposed Methodsmentioning

confidence: 99%

Image Classification Based on Light Convolutional Neural Network Using Pulse Couple Neural Network

Rafidison

Ramafiarisona

Randriamitantsoa

et al. 2023

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Recently, most image classification studies solicit the intervention of convolutional neural networks because these DL-based classification methods generally outperform other methodologies with higher accuracy. However, this type of deep learning networks require many parameters and have a complex structure with multiple convolutional and pooling layers depending on the objective. These layers compute a large volume of data and it may impact the processing time and the performance. Therefore, this paper proposes a new method of image classification based on the light convolutional neural network. It consists of replacing the feature extraction layers of standard convolutional neural network with a single pulse coupled neural network by introducing the notion of foveation. This module provides the feature map of input image and the data compression using Discrete Wavelet Transform which is an optional step depending on the information quantity of this signature. The fully connected neural network, which has six hidden layers, classifies the image. With this technique, the computation time is reduced, and the network architecture is identical and simple independent of the type of dataset. The number of parameter is less than that in current research. The proposed method was validated with different dataset such as Caltech-101, Caltech-256, CIFAR-10, CIFAR-100, and ImageNet, and the accuracy reaches 92%, 90%, 99%, 94%, and 91%, respectively, which are better than the previous related works.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

Image Classification Based on Light Convolutional Neural Network Using Pulse Couple Neural Network

Rafidison

Ramafiarisona

Randriamitantsoa

et al. 2023

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The authors' comparison analysis revealed that training CNN for image classification on parallelized CPU cores significantly reduced computation time. Because of their computationally expensive processing layers, deep learning architectures pose a significant challenge in practical implementation (Datta et al, 2020 ).…”

Section: Related Studymentioning

confidence: 99%

GAPCNN with HyPar: Global Average Pooling convolutional neural network with novel NNLU activation function and HYBRID parallelism

Habib

Qureshi

2022

Front. Comput. Neurosci.

View full text Add to dashboard Cite

With the increasing demand for deep learning in the last few years, CNNs have been widely used in many applications and have gained interest in classification, regression, and image recognition tasks. The training of these deep neural networks is compute-intensive and takes days or even weeks to train the model from scratch. The compute-intensive nature of these deep neural networks sometimes limits the practical implementation of CNNs in real-time applications. Therefore, the computational speedup in these networks is of utmost importance, which generates interest in CNN training acceleration. Much research is going on to meet the computational requirement and make it feasible for real-time applications. Because of its simplicity, data parallelism is used primarily, but it performs badly sometimes. In most cases, researchers prefer model parallelism to data parallelism, but it is not always the best choice. Therefore, in this study, we implement a hybrid of both data and model parallelism to improve the computational speed without compromising accuracy. There is only a 1.5% accuracy drop in our proposed study with an increased speed up of 3.62X. Also, a novel activation function Normalized Non-linear Activation Unit NNLU is proposed to introduce non-linearity in the model. The activation unit is non-saturated and helps avoid the model's over-fitting. The activation unit is free from the vanishing gradient problem. Also, the fully connected layer in the proposed CNN model is replaced by the Global Average Pooling layers (GAP) to enhance the model's accuracy and computational performance. When tested on a bio-medical image dataset, the model achieves an accuracy of 98.89% and requires a training time of only 1 s. The model categorizes medical images into different categories of glioma, meningioma, and pituitary tumor. The model is compared with existing state-of-art techniques, and it is observed that the proposed model outperforms others in classification accuracy and computational speed. Also, results are observed for different optimizers', different learning rates, and various epoch numbers.

show abstract

“…There are 13 convolutional layers and three fully connected layers. It also contains five max-pooling layers in the middle, and at the output, it has the Softmax activation function [28][29][30]. The entire module's architecture is divided into various sets of convolutional layers and max-pooling layers, following which the fully connected layer and the activation function are present.…”

Section: The Vgg16 Modelmentioning

confidence: 99%

Performance Comparison of Deep CNN Models for Detecting Driver’s Distraction

Srinivasan¹,

Garg²,

Datta³

et al. 2021

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

According to various worldwide statistics, most car accidents occur solely due to human error. The person driving a car needs to be alert, especially when travelling through high traffic volumes that permit high-speed transit since a slight distraction can cause a fatal accident. Even though semiautomated checks, such as speed detecting cameras and speed barriers, are deployed, controlling human errors is an arduous task. The key causes of driver's distraction include drunken driving, conversing with co-passengers, fatigue, and operating gadgets while driving. If these distractions are accurately predicted, the drivers can be alerted through an alarm system. Further, this research develops a deep convolutional neural network (deep CNN) models for predicting the reason behind the driver's distraction. The deep CNN models are trained using numerous images of distracted drivers. The performance of deep CNN models, namely the VGG16, ResNet, and Xception network, is assessed based on the evaluation metrics, such as the precision score, the recall/sensitivity score, the F1 score, and the specificity score. The ResNet model outperformed all other models as the best detection model for predicting and accurately determining the drivers' activities.

show abstract

Comparison of Performance of Parallel Computation of CPU Cores on CNN model

Cited by 10 publications

References 4 publications

Image Classification Based on Light Convolutional Neural Network Using Pulse Couple Neural Network

Image Classification Based on Light Convolutional Neural Network Using Pulse Couple Neural Network

GAPCNN with HyPar: Global Average Pooling convolutional neural network with novel NNLU activation function and HYBRID parallelism

Performance Comparison of Deep CNN Models for Detecting Driver’s Distraction

Contact Info

Product

Resources

About