scholarly journals Quantized Convolutional Neural Networks for Mobile Devices

2016 ◽  
Author(s):  
Jiaxiang Wu ◽  
Cong Leng ◽  
Yuhang Wang ◽  
Qinghao Hu ◽  
Jian Cheng
Keyword(s):  
Neural Networks ◽  
Mobile Devices ◽  
Convolutional Neural Networks

scholarly journals MB-CNN: Memristive Binary Convolutional Neural Networks for Embedded Mobile Devices

2018 ◽  
Vol 8 (4) ◽  
pp. 38 ◽  
Author(s):  
Arjun Pal Chowdhury ◽  
Pranav Kulkarni ◽  
Mahdi Nazm Bojnordi
Keyword(s):  
Neural Networks ◽  
Mobile Devices ◽  
Convolutional Neural Networks ◽  
Mobile User ◽  
Power Performance ◽  
Performance Requirements ◽  
System Energy ◽  
And Performance ◽  
The Cost

Applications of neural networks have gained significant importance in embedded mobile devices and Internet of Things (IoT) nodes. In particular, convolutional neural networks have emerged as one of the most powerful techniques in computer vision, speech recognition, and AI applications that can improve the mobile user experience. However, satisfying all power and performance requirements of such low power devices is a significant challenge. Recent work has shown that binarizing a neural network can significantly improve the memory requirements of mobile devices at the cost of minor loss in accuracy. This paper proposes MB-CNN, a memristive accelerator for binary convolutional neural networks that perform XNOR convolution in-situ novel 2R memristive data blocks to improve power, performance, and memory requirements of embedded mobile devices. The proposed accelerator achieves at least 13.26 × , 5.91 × , and 3.18 × improvements in the system energy efficiency (computed by energy × delay) over the state-of-the-art software, GPU, and PIM architectures, respectively. The solution architecture which integrates CPU, GPU and MB-CNN outperforms every other configuration in terms of system energy and execution time.

Plants recognition using embedded Convolutional Neural Networks on Mobile devices

2020 ◽  
Author(s):  
Denise Pechebovicz ◽  
Sthefanie Premebida ◽  
Vinicios Soares ◽  
Thiago Camargo ◽  
Jakson L. Bittencourt ◽  
...  
Keyword(s):  
Neural Networks ◽  
Mobile Devices ◽  
Convolutional Neural Networks

scholarly journals Deep Sensing: Inertia and Ambient Sensing for Activity Context Recognition Using Deep Convolutional Neural Networks

2020 ◽  
Author(s):  
Abayomi Otebolaku ◽  
Timibloudi Enamamu ◽  
Ali Alfouldi ◽  
Augustine Ikpehai ◽  
Jims Marchang
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Mobile Devices ◽  
Convolutional Neural Networks ◽  
Inertial Sensor ◽  
Deep Convolutional Neural Networks ◽  
Context Recognition ◽  
Embedded Sensing ◽  
Sensor Signals ◽  
Ambient Sensing

With the widespread of embedded sensing capabilities of mobile devices, there has been unprecedented development of context-aware solutions. This allows the proliferation of various intelligent applications such as those for remote health and lifestyle monitoring, intelligent personalized services, etc. However, activity context recognition based on multivariate time series signals obtained from mobile devices in unconstrained conditions is naturally prone to imbalance class problems. This means that recognition models tend to predict classes with the majority number of samples whilst ignoring classes with the least number of samples, resulting in poor generalization. To address this problem, we propose to augment the time series signals from inertia sensors with signals from ambient sensing to train deep convolutional neural networks (DCNN) models. DCNN provides the characteristics that capture local dependency and scale invariance of these combined sensor signals. Consequently, we developed a DCNN model using only inertial sensor signals and then developed another model that combined signals from both inertia and ambient sensors aiming to investigate the class imbalance problem by improving the performance of the recognition model. Evaluation and analysis of the proposed system using data with imbalanced classes show that the system achieved better recognition accuracy when data from inertial sensors are combined with those from ambient sensors such as environment noise level and illumination, with an overall improvement of 5.3% accuracy.

scholarly journals APPLICATION OF THE CONVOLUTIONAL NEURAL NETWORKS FOR THE SECURITY OF THE OBJECT RECOGNITION IN A VIDEO STREAM

2020 ◽  
Vol 4 (8) ◽  
pp. 97-112
Author(s):  
Danylo Svatiuk ◽  
Oksana Svatiuk ◽  
Oleksandr Belei
Keyword(s):  
Operating System ◽  
Neural Networks ◽  
Real Time ◽  
Mobile Devices ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Video Stream ◽  
License Plate ◽  
License Plate Recognition ◽  
Android Operating System

The article is devoted to analyzing methods for recognizing images and finding them in the video stream. The evolution of the structure of convolutional neural networks used in the field of computer video flow diagnostics is analyzed. The performance of video flow diagnostics algorithms and car license plate recognition has been evaluated. The technique of recognizing the license plates of cars in the video stream of transport neural networks is described. The study focuses on the creation of a combined system that combines artificial intelligence and computer vision based on fuzzy logic. To solve the problem of license plate image recognition in the video stream of the transport system, a method of image recognition in a continuous video stream with its implementation based on the composition of traditional image processing methods and neural networks with convolutional and periodic layers is proposed. The structure and peculiarities of functioning of the intelligent distributed system of urban transport safety, which feature is the use of mobile devices connected to a single network, are described. A practical implementation of a software application for recognizing car license plates by mobile devices on the Android operating system platform has been proposed and implemented. Various real-time vehicle license plate recognition scenarios have been developed and stored in a database for further analysis and use. The proposed application uses two different specialized neural networks: one for detecting objects in the video stream, the other for recognizing text from the selected image. Testing and analysis of software applications on the Android operating system platform for license plate recognition in real time confirmed the functionality of the proposed mathematical software and can be used to securely analyze the license plates of cars in the scanned video stream by comparing with license plates in the existing database. The authors have implemented the operation of the method of convolutional neural networks detection and recognition of license plates, personnel and critical situations in the video stream from cameras of mobile devices in real time. The possibility of its application in the field of safe identification of car license plates has been demonstrated.

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