scholarly journals Compact Convolutional Neural Network Accelerator for IoT Endpoint SoC

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 497 ◽  
Author(s):  
Fen Ge ◽  
Ning Wu ◽  
Hao Xiao ◽  
Yuanyuan Zhang ◽  
Fang Zhou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Functional Modules ◽  
Computational Power ◽  
System A ◽  
Unit Design ◽  
Artificial Intelligence Algorithm ◽  
And Performance ◽  
On Chip ◽  
The Internet Of Things

As a classical artificial intelligence algorithm, the convolutional neural network (CNN) algorithm plays an important role in image recognition and classification and is gradually being applied in the Internet of Things (IoT) system. A compact CNN accelerator for the IoT endpoint System-on-Chip (SoC) is proposed in this paper to meet the needs of CNN computations. Based on analysis of the CNN structure, basic functional modules of CNN such as convolution circuit and pooling circuit with a low data bandwidth and a smaller area are designed, and an accelerator is constructed in the form of four acceleration chains. After the acceleration unit design is completed, the Cortex-M3 is used to construct a verification SoC and the designed verification platform is implemented on the FPGA to evaluate the resource consumption and performance analysis of the CNN accelerator. The CNN accelerator achieved a throughput of 6.54 GOPS (giga operations per second) by consuming 4901 LUTs without using any hardware multipliers. The comparison shows that the compact accelerator proposed in this paper makes the CNN computational power of the SoC based on the Cortex-M3 kernel two times higher than the quad-core Cortex-A7 SoC and 67% of the computational power of eight-core Cortex-A53 SoC.

scholarly journals A Reconfigurable Convolutional Neural Network-Accelerated Coprocessor Based on RISC-V Instruction Set

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1005 ◽  
Author(s):  
Ning Wu ◽  
Tao Jiang ◽  
Lei Zhang ◽  
Fang Zhou ◽  
Fen Ge
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Instruction Set ◽  
C Language ◽  
Standard Instruction ◽  
Artificial Intelligence Algorithm ◽  
And Performance ◽  
Xilinx Fpga ◽  
The Internet Of Things ◽  
Better Than

As a typical artificial intelligence algorithm, the convolutional neural network (CNN) is widely used in the Internet of Things (IoT) system. In order to improve the computing ability of an IoT CPU, this paper designs a reconfigurable CNN-accelerated coprocessor based on the RISC-V instruction set. The interconnection structure of the acceleration chain designed by the predecessors is optimized, and the accelerator is connected to the RISC-V CPU core in the form of a coprocessor. The corresponding instruction of the coprocessor is designed and the instruction compiling environment is established. Through the inline assembly in the C language, the coprocessor instructions are called, coprocessor acceleration library functions are established, and common algorithms in the IoT system are implemented on the coprocessor. Finally, resource consumption evaluation and performance analysis of the coprocessor are completed on a Xilinx FPGA. The evaluation results show that the reconfigurable CNN-accelerated coprocessor only consumes 8534 LUTS, accounting for 47.6% of the total SoC system. The number of instruction cycles required to implement functions such as convolution and pooling based on the designed coprocessor instructions is better than using the standard instruction set, and the acceleration ratio of convolution is 6.27 times that of the standard instruction set.

scholarly journals Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2648
Author(s):  
Muhammad Aamir ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
Muhammad Zeeshan Azam ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Natural Disasters ◽  
Deep Convolutional Neural Network ◽  
Multispectral Images ◽  
Learning Techniques ◽  
Proposed Model ◽  
Disaster Intensity ◽  
And Performance

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

A Survey on Supervised Convolutional Neural Network and Its Major Applications

2020 ◽  
pp. 1058-1071
Author(s):  
D. T. Mane ◽  
U. V. Kulkarni
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Data Science ◽  
Medical Image Analysis ◽  
Computational Power ◽  
Science Field ◽  
Network Applications ◽  
Learning Techniques ◽  
Neural Network Applications

With the advances in the computer science field, various new data science techniques have been emerged. Convolutional Neural Network (CNN) is one of the Deep Learning techniques which have captured lots of attention as far as real world applications are considered. It is nothing but the multilayer architecture with hidden computational power which detects features itself. It doesn't require any handcrafted features. The remarkable increase in the computational power of Convolutional Neural Network is due to the use of Graphics processor units, parallel computing, also the availability of large amount of data in various variety forms. This paper gives the broad view of various supervised Convolutional Neural Network applications with its salient features in the fields, mainly Computer vision for Pattern and Object Detection, Natural Language Processing, Speech Recognition, Medical image analysis.

scholarly journals Fast and Efficient Edge Fusing Network Architectures for Accurate Single Image Super-resolution

2021 ◽  
Author(s):  
Debjoy Chowdhury
Keyword(s):  
Neural Network ◽  
Receptive Field ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Performance Enhancement ◽  
Super Resolution ◽  
Main Concept ◽  
High Scale ◽  
Image Super Resolution ◽  
And Performance

Recovering a High-Resolution (HR) image from a Low-Resolution (LR) image is the main concept of image Super-Resolution (SR). Convolution Neural Networks (CNN) are becoming widely adopted in many applications including generation of HR images from LR images. Although CNNs are widely used with great performance improvements, there is still much room for improvement. There has always been a trade-off between the number of parameters and performance enhancement. This thesis presents a novel convolutional neural network architecture for high scale image SR inspired by the DenseNet and ResNet architecture. In particular, modifications can be made to the convolutional layers in the network: stacking the features and reusing the weight layers to increase the receptive field. It is shown how this method can be used to expand the receptive field and performance of super-resolution networks, without increasing the number of trainable parameters and sacrificing the computation time. These modifications can easily be integrated into any convolutional neural network to improve the accuracy by efficient high-level feature extraction while reducing training time and parameter numbers. Proposed methods are especially effective for the challenging high scale SR due to edge and texture recovery through the expanded network receptive field. Experimental results show that the proposed model outperforms the state-of-the-art methods.

scholarly journals Fast and Efficient Edge Fusing Network Architectures for Accurate Single Image Super-resolution

2021 ◽  
Author(s):  
Debjoy Chowdhury
Keyword(s):  
Neural Network ◽  
Receptive Field ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Performance Enhancement ◽  
Super Resolution ◽  
Main Concept ◽  
High Scale ◽  
Image Super Resolution ◽  
And Performance

Recovering a High-Resolution (HR) image from a Low-Resolution (LR) image is the main concept of image Super-Resolution (SR). Convolution Neural Networks (CNN) are becoming widely adopted in many applications including generation of HR images from LR images. Although CNNs are widely used with great performance improvements, there is still much room for improvement. There has always been a trade-off between the number of parameters and performance enhancement. This thesis presents a novel convolutional neural network architecture for high scale image SR inspired by the DenseNet and ResNet architecture. In particular, modifications can be made to the convolutional layers in the network: stacking the features and reusing the weight layers to increase the receptive field. It is shown how this method can be used to expand the receptive field and performance of super-resolution networks, without increasing the number of trainable parameters and sacrificing the computation time. These modifications can easily be integrated into any convolutional neural network to improve the accuracy by efficient high-level feature extraction while reducing training time and parameter numbers. Proposed methods are especially effective for the challenging high scale SR due to edge and texture recovery through the expanded network receptive field. Experimental results show that the proposed model outperforms the state-of-the-art methods.

scholarly journals System on Chip (SoC) for Invisible Electrocardiography (ECG) Biometrics

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 348
Author(s):  
Francisco de Melo ◽  
Horácio C. Neto ◽  
Hugo Plácido da Silva
Keyword(s):  
Neural Network ◽  
Embedded System ◽  
Convolutional Neural Network ◽  
Limited Resource ◽  
Security Systems ◽  
Fundamental Building Block ◽  
Computational Performance ◽  
Current State ◽  
System On A Chip ◽  
On Chip

Biometric identification systems are a fundamental building block of modern security. However, conventional biometric methods cannot easily cope with their intrinsic security liabilities, as they can be affected by environmental factors, can be easily “fooled” by artificial replicas, among other caveats. This has lead researchers to explore other modalities, in particular based on physiological signals. Electrocardiography (ECG) has seen a growing interest, and many ECG-enabled security identification devices have been proposed in recent years, as electrocardiography signals are, in particular, a very appealing solution for today’s demanding security systems—mainly due to the intrinsic aliveness detection advantages. These Electrocardiography (ECG)-enabled devices often need to meet small size, low throughput, and power constraints (e.g., battery-powered), thus needing to be both resource and energy-efficient. However, to date little attention has been given to the computational performance, in particular targeting the deployment with edge processing in limited resource devices. As such, this work proposes an implementation of an Artificial Intelligence (AI)-enabled ECG-based identification embedded system, composed of a RISC-V based System-on-a-Chip (SoC). A Binary Convolutional Neural Network (BCNN) was implemented in our SoC’s hardware accelerator that, when compared to a software implementation of a conventional, non-binarized, Convolutional Neural Network (CNN) version of our network, achieves a 176,270× speedup, arguably outperforming all the current state-of-the-art CNN-based ECG identification methods.

scholarly journals Intracavitary Electrocardiogram Guidance Aids Excavation of Rhythm Abnormalities in Patients with Occult Heart Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yanli Wei ◽  
Ying Zhu ◽  
Xin Wen ◽  
Qing Rui ◽  
Wei Hu
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Convolutional Neural Network ◽  
Automatic Classification ◽  
Classification Algorithm ◽  
Deep Convolutional Neural Network ◽  
Advantages And Disadvantages ◽  
Electrocardiogram Signals ◽  
Electrocardiogram Signal ◽  
And Performance

In this paper, the analysis of intracavitary electrocardiograms is used to guide the mining of abnormal cardiac rhythms in patients with hidden heart disease, and the algorithm is improved to address the data imbalance problem existing in the abnormal electrocardiogram signals, and a weight-based automatic classification algorithm for deep convolutional neural network electrocardiogram signals is proposed. By preprocessing the electrocardiogram data from the MIT-BIH arrhythmia database, the experimental dataset training algorithm model is obtained, and the algorithm model is migrated into the project. In terms of system design and implementation, by comparing the advantages and disadvantages of the electrocardiogram monitoring system platform, the overall design of the system was carried out in terms of functional and performance requirements according to the system realization goal, and a mobile platform system capable of classifying common abnormal electrocardiogram signals was developed. The system is capable of long-term monitoring and can invoke the automatic classification algorithm model of electrocardiogram signals for analysis. In this paper, the functional logic test and performance test were conducted on the main functional modules of the system. The test results show that the system can run stably and monitor electrocardiogram signals for a long time and can correctly call the deep convolutional neural network-based automatic electrocardiogram signal classification algorithm to analyze the electrocardiogram signals and achieve the requirements of displaying the electrocardiogram signal waveform, analyzing the heartbeat type, and calculating the average heart rate, which achieves the goal of real-time continuous monitoring and analysis of the electrocardiogram signals.

scholarly journals Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 43 ◽  
Author(s):  
Naveed Ilyas ◽  
Ahsan Shahzad ◽  
Kiseon Kim
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Convolutional Neural Network ◽  
Smart Cities ◽  
Future Research ◽  
Crowd Counting ◽  
Crowd Management ◽  
Adaptive Monitoring ◽  
Potential Applications ◽  
And Performance

Traditional handcrafted crowd-counting techniques in an image are currently transformed via machine-learning and artificial-intelligence techniques into intelligent crowd-counting techniques. This paradigm shift offers many advanced features in terms of adaptive monitoring and the control of dynamic crowd gatherings. Adaptive monitoring, identification/recognition, and the management of diverse crowd gatherings can improve many crowd-management-related tasks in terms of efficiency, capacity, reliability, and safety. Despite many challenges, such as occlusion, clutter, and irregular object distribution and nonuniform object scale, convolutional neural networks are a promising technology for intelligent image crowd counting and analysis. In this article, we review, categorize, analyze (limitations and distinctive features), and provide a detailed performance evaluation of the latest convolutional-neural-network-based crowd-counting techniques. We also highlight the potential applications of convolutional-neural-network-based crowd-counting techniques. Finally, we conclude this article by presenting our key observations, providing strong foundation for future research directions while designing convolutional-neural-network-based crowd-counting techniques. Further, the article discusses new advancements toward understanding crowd counting in smart cities using the Internet of Things (IoT).

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