scholarly journals Modeling and characterizing novel pulsed neural networks for real-time applications

2011 ◽  
Author(s):  
Hualiang Zhuang
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Pulsed Neural Networks ◽  
Real Time Applications

Meta-Heuristic Parameter Optimization for ANN and Real-Time Applications of ANN

2022 ◽  
pp. 166-201
Author(s):  
Asha Gowda Karegowda ◽  
Devika G.
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Parameter Optimization ◽  
Heuristic Algorithms ◽  
Search Space ◽  
Optimization Method ◽  
Classification Problems ◽  
Local Optima ◽  
Consistent Solution ◽  
Real Time Applications

Artificial neural networks (ANN) are often more suitable for classification problems. Even then, training of ANN is a surviving challenge task for large and high dimensional natured search space problems. These hitches are more for applications that involves process of fine tuning of ANN control parameters: weights and bias. There is no single search and optimization method that suits the weights and bias of ANN for all the problems. The traditional heuristic approach fails because of their poorer convergence speed and chances of ending up with local optima. In this connection, the meta-heuristic algorithms prove to provide consistent solution for optimizing ANN training parameters. This chapter will provide critics on both heuristics and meta-heuristic existing literature for training neural networks algorithms, applicability, and reliability on parameter optimization. In addition, the real-time applications of ANN will be presented. Finally, future directions to be explored in the field of ANN are presented which will of potential interest for upcoming researchers.

scholarly journals Real-time Neural Networks Implementation Proposal for Microcontrollers

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1597
Author(s):  
Caio José B. V. Guimarães ◽  
Marcelo A. C. Fernandes
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Intelligent Systems ◽  
Implementation Strategy ◽  
Processing Time ◽  
Mean Squared Error ◽  
Low Cost ◽  
Real Time Applications ◽  
Time Required ◽  
Process Classification

The adoption of intelligent systems with Artificial Neural Networks (ANNs) embedded in hardware for real-time applications currently faces a growing demand in fields such as the Internet of Things (IoT) and Machine to Machine (M2M). However, the application of ANNs in this type of system poses a significant challenge due to the high computational power required to process its basic operations. This paper aims to show an implementation strategy of a Multilayer Perceptron (MLP)-type neural network, in a microcontroller (a low-cost, low-power platform). A modular matrix-based MLP with the full classification process was implemented as was the backpropagation training in the microcontroller. The testing and validation were performed through Hardware-In-the-Loop (HIL) of the Mean Squared Error (MSE) of the training process, classification results, and the processing time of each implementation module. The results revealed a linear relationship between the values of the hyperparameters and the processing time required for classification, also the processing time concurs with the required time for many applications in the fields mentioned above. These findings show that this implementation strategy and this platform can be applied successfully in real-time applications that require the capabilities of ANNs.

scholarly journals A Homogeneous Algorithm for Motion Estimation and Compensation by Using Cellular Neural Networks

2010 ◽  
Vol 5 (5) ◽  
pp. 719 ◽  
Author(s):  
Cristian Grava ◽  
Alexandru Gacsádi ◽  
Ioan Buciu
Keyword(s):  
Neural Networks ◽  
Motion Estimation ◽  
Real Time ◽  
Cellular Neural Networks ◽  
Image Sequences ◽  
Artificial Vision ◽  
Real Time Image ◽  
Field Programmable ◽  
Real Time Applications ◽  
Time Image

In this paper we present an original implementation of a homogeneous algorithm for motion estimation and compensation in image sequences, by using Cellular Neural Networks (CNN). The CNN has been proven their efficiency in real-time image processing, because they can be implemented on a CNN chip or they can be emulated on Field Programmable Gate Array (FPGA). The motion information is obtained by using a CNN implementation of the well-known Horn & Schunck method. This information is further used in a CNN implementation of a motion-compensation method. Through our algorithm we obtain a homogeneous implementation for real-time applications in artificial vision or medical imaging. The algorithm is illustrated on some classical sequences and the results confirm the validity of our algorithm.

scholarly journals Noise and Performances Analysis of Commerical Aircrafts using Artificial Neural Networks

2015 ◽  
Vol 2 (1-2.) ◽  
Author(s):  
Şahin Yildirim
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
High Technology ◽  
Measured Parameter ◽  
The World ◽  
Real Time Applications ◽  
Artificial Neural ◽  
Parameter Values

Commerical aircrafts are very important part for airway travelling. In spite of high technology on aircrafts, there is still fatality accidents in the world. Because of this reason, it is very important criteria to analyse noises of main elements of the air-craft systems. In tis study, an aircraft’s main disturbances are analysed with proposed neural networks. Firstly, the noises of the jet, turbine and fan were measured from the aircraft. Secondly, the measured parameter values were predicted the proposed neural networks. The results of the proposed neuarl approaches were shown that this type of neural predictors will be employed to predict aircrafts unpredicted disturbances in real time applications.

scholarly journals NICE: Noise Injection and Clamping Estimation for Neural Network Quantization

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2144
Author(s):  
Chaim Baskin ◽  
Evgenii Zheltonozhkii ◽  
Tal Rozen ◽  
Natan Liss ◽  
Yoav Chai ◽  
...  
Keyword(s):  
Neural Networks ◽  
Power Systems ◽  
Low Power ◽  
Real Time ◽  
Language Processing ◽  
Power Efficient ◽  
Noise Injection ◽  
Classification Tasks ◽  
Real Time Applications ◽  
The Cost

Convolutional Neural Networks (CNNs) are very popular in many fields including computer vision, speech recognition, natural language processing, etc. Though deep learning leads to groundbreaking performance in those domains, the networks used are very computationally demanding and are far from being able to perform in real-time applications even on a GPU, which is not power efficient and therefore does not suit low power systems such as mobile devices. To overcome this challenge, some solutions have been proposed for quantizing the weights and activations of these networks, which accelerate the runtime significantly. Yet, this acceleration comes at the cost of a larger error unless spatial adjustments are carried out. The method proposed in this work trains quantized neural networks by noise injection and a learned clamping, which improve accuracy. This leads to state-of-the-art results on various regression and classification tasks, e.g., ImageNet classification with architectures such as ResNet-18/34/50 with as low as 3 bit weights and activations. We implement the proposed solution on an FPGA to demonstrate its applicability for low-power real-time applications. The quantization code will become publicly available upon acceptance.

Integration of Optimization Approach Based on Multiple Wordlength Operation Grouping in the AAA Methodology for Real-Time Systems

2014 ◽  
Vol 5 (1) ◽  
pp. 37-60
Author(s):  
Ahmed Ghazi Blaiech ◽  
Khaled Ben Khalifa ◽  
Mohamed Boubaker ◽  
Mohamed Akil ◽  
Mohamed Hedi Bedoui
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Vector Quantization ◽  
Field Programmable Gate Array ◽  
Optimization Approach ◽  
Real Time Systems ◽  
Reconfigurable Circuits ◽  
Field Programmable ◽  
Real Time Applications ◽  
Time Systems

The Multiple-Wordlength Operation Grouping (MWOG) is a recently used approach for an optimized implementation on a Field Programmable Gate Array (FPGA). By fixing the precision constraint, this approach allows minimizing the data wordlength. In this paper, the authors present the integration of the approach based on the MWOG in the Algorithm Architecture Adequation (AAA) methodology, designed to implement real-time applications onto reconfigurable circuits. This new AAA-MWOG methodology will improve the optimization phase of the AAA methodology by taking into account the data wordlength and creating approximative-wordlength operation groups, where the operations in the same group will be performed with the same operator. The AAA-MWOG methodology will allow a considerable gain of circuit resources. This contribution is demonstrated by implementing the Learning Vector Quantization (LVQ) neural-networks model on the FPGA. The LVQ optimization is used to quantify vigilance states starting from processing the electroencephalographic signal. The precision-gain relation has been studied and reported.

Meta-Heuristic Parameter Optimization for ANN and Real-Time Applications of ANN

2021 ◽  
pp. 227-269
Author(s):  
Asha Gowda Karegowda ◽  
Devika G.
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Parameter Optimization ◽  
Heuristic Algorithms ◽  
Search Space ◽  
Optimization Method ◽  
Classification Problems ◽  
Local Optima ◽  
Consistent Solution ◽  
Real Time Applications

Artificial neural networks (ANN) are often more suitable for classification problems. Even then, training of ANN is a surviving challenge task for large and high dimensional natured search space problems. These hitches are more for applications that involves process of fine tuning of ANN control parameters: weights and bias. There is no single search and optimization method that suits the weights and bias of ANN for all the problems. The traditional heuristic approach fails because of their poorer convergence speed and chances of ending up with local optima. In this connection, the meta-heuristic algorithms prove to provide consistent solution for optimizing ANN training parameters. This chapter will provide critics on both heuristics and meta-heuristic existing literature for training neural networks algorithms, applicability, and reliability on parameter optimization. In addition, the real-time applications of ANN will be presented. Finally, future directions to be explored in the field of ANN are presented which will of potential interest for upcoming researchers.

Maintaining Consistency Over A Network in Real-Time Applications

1989 ◽  
Author(s):  
Insup Lee ◽  
Susan Davidson ◽  
Victor Wolfe
Keyword(s):  
Real Time ◽  
Real Time Applications
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