scholarly journals Extensible Embedded Processor for Convolutional Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Joshua Misko ◽  
Shrikant S. Jadhav ◽  
Youngsoo Kim
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Search Algorithm ◽  
Recognition System ◽  
Synthetic Aperture ◽  
Embedded Processor ◽  
Computing Power ◽  
Execution Speed ◽  
Facial Recognition System

Convolutional neural networks (CNNs) require significant computing power during inference. Smart phones, for example, may not run a facial recognition system or search algorithm smoothly due to the lack of resources and supporting hardware. Methods for reducing memory size and increasing execution speed have been explored, but choosing effective techniques for an application requires extensive knowledge of the network architecture. This paper proposes a general approach to preparing a compressed deep neural network processor for inference with minimal additions to existing microprocessor hardware. To show the benefits to the proposed approach, an example CNN for synthetic aperture radar target classification is modified and complimentary custom processor instructions are designed. The modified CNN is examined to show the effects of the modifications and the custom processor instructions are profiled to illustrate the potential performance increase from the new extended instructions.

scholarly journals Designing convolutional neural networks with constrained evolutionary piecemeal training

2021 ◽  
Author(s):  
Dolly Sapra ◽  
Andy D. Pimentel
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Language Processing ◽  
Network Architecture ◽  
High Performance ◽  
Search Algorithm ◽  
Search Space ◽  
Pareto Optimal Set

AbstractThe automated architecture search methodology for neural networks is known as Neural Architecture Search (NAS). In recent times, Convolutional Neural Networks (CNNs) designed through NAS methodologies have achieved very high performance in several fields, for instance image classification and natural language processing. Our work is in the same domain of NAS, where we traverse the search space of neural network architectures with the help of an evolutionary algorithm which has been augmented with a novel approach of piecemeal-training. In contrast to the previously published NAS techniques, wherein the training with given data is considered an isolated task to estimate the performance of neural networks, our work demonstrates that a neural network architecture and the related weights can be jointly learned by combining concepts of the traditional training process and evolutionary architecture search in a single algorithm. The consolidation has been realised by breaking down the conventional training technique into smaller slices and collating them together with an integrated evolutionary architecture search algorithm. The constraints on architecture search space are placed by limiting its various parameters within a specified range of values, consequently regulating the neural network’s size and memory requirements. We validate this concept on two vastly different datasets, namely, the CIFAR-10 dataset in the domain of image classification, and PAMAP2 dataset in the Human Activity Recognition (HAR) domain. Starting from randomly initialized and untrained CNNs, the algorithm discovers models with competent architectures, which after complete training, reach an accuracy of of 92.5% for CIFAR-10 and 94.36% PAMAP2. We further extend the algorithm to include an additional conflicting search objective: the number of parameters of the neural network. Our multi-objective algorithm produces a Pareto optimal set of neural networks, by optimizing the search for both the accuracy and the parameter count, thus emphasizing the versatility of our approach.

Cancelable Iris Recognition System with Pre-trained Convolutional Neural Networks

2019 ◽  
Vol 28 (1) ◽  
pp. 95-101
Author(s):  
Eman M. Omran ◽  
Randa F. Soliman ◽  
Ayman A. Eisa ◽  
Nabil A. Ismail ◽  
Fathi E. Abd El-Samie
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Iris Recognition ◽  
Recognition System

scholarly journals X-ray image based pneumonia classification using convolutional neural networks

2020 ◽  
Vol 6 (20) ◽  
pp. 54-67
Author(s):  
Sarah Badr AlSumairi ◽  
Mohamed Maher Ben Ismail
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Data Augmentation ◽  
Critical Factor ◽  
Classification Problem ◽  
X Rays ◽  
X Ray ◽  
Erroneous Decision ◽  
Short Period

Pneumonia is an infectious disease of the lungs. About one third to one half of pneumonia cases are caused by bacteria. Early diagnosis is a critical factor for a successful treatment process. Typically, the disease can be diagnosed by a radiologist using chest X-ray images. In fact, chest X-rays are currently the best available method for diagnosing pneumonia. However, the recognition of pneumonia symptoms is a challenging task that relies on the availability of expert radiologists. Such “human” diagnosis can be inaccurate and subjective due to lack of clarity and erroneous decision. Moreover, the error can increase more if the physician is requested to analyze tens of X-rays within a short period of time. Therefore, Computer-Aided Diagnosis (CAD) systems were introduced to support and assist physicians and make their efforts more productive. In this paper, we investigate, design, implement and assess customized Convolutional Neural Networks to overcome the image-based Pneumonia classification problem. Namely, ResNet-50 and DenseNet-161 models were inherited to design customized deep network architecture and improve the overall pneumonia classification accuracy. Moreover, data augmentation was deployed and associated with standard datasets to assess the proposed models. Besides, standard performance measures were used to validate and evaluate the proposed system.

scholarly journals A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural Networks

2020 ◽  
Author(s):  
B Wang ◽  
Y Sun ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Learning Task ◽  
Fixed Number ◽  
Learning Rate ◽  
Current Layer ◽  
Training Process ◽  
Deep Convolutional Neural Networks

© 2019, Springer Nature Switzerland AG. Image classification is a difficult machine learning task, where Convolutional Neural Networks (CNNs) have been applied for over 20 years in order to solve the problem. In recent years, instead of the traditional way of only connecting the current layer with its next layer, shortcut connections have been proposed to connect the current layer with its forward layers apart from its next layer, which has been proved to be able to facilitate the training process of deep CNNs. However, there are various ways to build the shortcut connections, it is hard to manually design the best shortcut connections when solving a particular problem, especially given the design of the network architecture is already very challenging. In this paper, a hybrid evolutionary computation (EC) method is proposed to automatically evolve both the architecture of deep CNNs and the shortcut connections. Three major contributions of this work are: Firstly, a new encoding strategy is proposed to encode a CNN, where the architecture and the shortcut connections are encoded separately; Secondly, a hybrid two-level EC method, which combines particle swarm optimisation and genetic algorithms, is developed to search for the optimal CNNs; Lastly, an adjustable learning rate is introduced for the fitness evaluations, which provides a better learning rate for the training process given a fixed number of epochs. The proposed algorithm is evaluated on three widely used benchmark datasets of image classification and compared with 12 peer Non-EC based competitors and one EC based competitor. The experimental results demonstrate that the proposed method outperforms all of the peer competitors in terms of classification accuracy.

scholarly journals A Survey on Techniques used in Medical Imaging Processing

2021 ◽  
Vol 2089 (1) ◽  
pp. 012013
Author(s):  
Priyadarshini Chatterjee ◽  
Dutta Sushama Rani
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Analysis ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Medical Image Analysis ◽  
Artificial Neural ◽  
Automated Screening ◽  
To Come

Abstract Automated diagnosis of diseases in the recent years have gain lots of advantages and potential. Specially automated screening of cancers has helped the clinicians over the time. Sometimes it is seen that the diagnosis of the clinicians is biased but automated detection can help them to come to a proper conclusion. Automated screening is implemented using either artificial inter connected system or convolutional inter connected system. As Artificial neural network is slow in computation, so Convolutional Neural Network has achieved lots of importance in the recent years. It is also seen that Convolutional Neural Network architecture requires a smaller number of datasets. This also provides them an edge over Artificial Neural Networks. Convolutional Neural Networks is used for both segmentation and classification. Image dissection is one of the important steps in the model used for any kind of image analysis. This paper surveys various such Convolutional Neural Networks that are used for medical image analysis.

scholarly journals Using Convolutional Neural Networks to Automate Aircraft Maintenance Visual Inspection

Aerospace ◽  
2020 ◽  
Vol 7 (12) ◽  
pp. 171
Author(s):  
Anil Doğru ◽  
Soufiane Bouarfa ◽  
Ridwan Arizar ◽  
Reyhan Aydoğan
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Visual Inspection ◽  
Hybrid Approach ◽  
Lightning Strike ◽  
Aircraft Maintenance ◽  
Original Dataset ◽  
Wide Range ◽  
Augmentation Techniques

Convolutional Neural Networks combined with autonomous drones are increasingly seen as enablers of partially automating the aircraft maintenance visual inspection process. Such an innovative concept can have a significant impact on aircraft operations. Though supporting aircraft maintenance engineers detect and classify a wide range of defects, the time spent on inspection can significantly be reduced. Examples of defects that can be automatically detected include aircraft dents, paint defects, cracks and holes, and lightning strike damage. Additionally, this concept could also increase the accuracy of damage detection and reduce the number of aircraft inspection incidents related to human factors like fatigue and time pressure. In our previous work, we have applied a recent Convolutional Neural Network architecture known by MASK R-CNN to detect aircraft dents. MASK-RCNN was chosen because it enables the detection of multiple objects in an image while simultaneously generating a segmentation mask for each instance. The previously obtained F1 and F2 scores were 62.67% and 59.35%, respectively. This paper extends the previous work by applying different techniques to improve and evaluate prediction performance experimentally. The approach uses include (1) Balancing the original dataset by adding images without dents; (2) Increasing data homogeneity by focusing on wing images only; (3) Exploring the potential of three augmentation techniques in improving model performance namely flipping, rotating, and blurring; and (4) using a pre-classifier in combination with MASK R-CNN. The results show that a hybrid approach combining MASK R-CNN and augmentation techniques leads to an improved performance with an F1 score of (67.50%) and F2 score of (66.37%).

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