scholarly journals Research on Model Compression for Embedded Platform through Quantization and Pruning

2021 ◽  
Vol 2078 (1) ◽  
pp. 012047
Author(s):  
Xiao Hu ◽  
Hao Wen
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Learning Model ◽  
Model Simplification ◽  
Accuracy Rate ◽  
Embedded Platform ◽  
Remarkable Improvement ◽  
Model Compression ◽  
Artificial Intelligence Technology ◽  
Deep Learning Model

Abstract So far, artificial intelligence has gone through decades of development. Although artificial intelligence technology is not yet mature, it has already been applied in many walks of life. With the explosion of IoT technology in 2019, artificial intelligence has ushered in a new climax. It can be said that the development of IoT technology has led to the development of artificial intelligence once again. But the traditional deep learning model is very complex and redundant. The hardware environment of IoT can not afford the time and resources cost by the model which runs on the GPU originally, so model compression without decreasing accuracy rate so much is applicable in this situation. In this paper, we experimented with using two tricks for model compression: Pruning and Quantization. By utilizing these methods, we got a remarkable improvement in model simplification while retaining a relatively close accuracy.

scholarly journals Patent Analysis Using Vector Space Model and Deep Learning Model : A Case of Artificial Intelligence Industry Technology

2021 ◽  
Author(s):  
Yongmin Yoo ◽  
Dongjin Lim ◽  
Kyungsun Kim
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Vector Space ◽  
Rapid Development ◽  
Vector Space Model ◽  
Learning Model ◽  
Patent Data ◽  
Space Model ◽  
Artificial Intelligence Technology ◽  
Deep Learning Model

Thanks to rapid development of artificial intelligence technology in recent years, the current artificial intelligence technology is contributing to many part of society. Education, environment, medical care, military, tourism, economy, politics, etc. are having a very large impact on society as a whole. For example, in the field of education, there is an artificial intelligence tutoring system that automatically assigns tutors based on student's level. In the field of economics, there are quantitative investment methods that automatically analyze large amounts of data to find investment laws to create investment models or predict changes in financial markets. As such, artificial intelligence technology is being used in various fields. So, it is very important to know exactly what factors have an important influence on each field of artificial intelligence technology and how the relationship between each field is connected. Therefore, it is necessary to analyze artificial intelligence technology in each field. In this paper, we analyze patent documents related to artificial intelligence technology. We propose a method for keyword analysis within factors using artificial intelligence patent data sets for artificial intelligence technology analysis. This is a model that relies on feature engineering based on deep learning model named KeyBERT, and using vector space model. A case study of collecting and analyzing artificial intelligence patent data was conducted to show how the proposed model can be applied to real-world problems.

scholarly journals Application of quantisation-based deep-learning model compression in JPEG image steganalysis

2018 ◽  
Vol 2018 (16) ◽  
pp. 1402-1406 ◽  
Author(s):  
Xiancheng Wu ◽  
Zilong Shao ◽  
Pei Ou ◽  
Shunquan Tan
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Image Steganalysis ◽  
Jpeg Image ◽  
Model Compression ◽  
Deep Learning Model

scholarly journals Advanced Deep Learning Model

2021 ◽  
Author(s):  
Yew Kee Wong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Learning Model ◽  
Intelligence Analysis ◽  
Basic Parameters ◽  
Deep Learning Model ◽  
Learning Sets

Deep learning is a type of machine learning that trains a computer to perform human-like tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing. This paper aims to illustrate some of the different deep learning algorithms and methods which can be applied to artificial intelligence analysis, as well as the opportunities provided by the application in various decision making domains.

Combining deep learning model compression techniques

2022 ◽  
Vol 20 (3) ◽  
pp. 458-464
Author(s):  
Jose Vitor Santos Silva ◽  
Leonardo Matos Matos ◽  
Flavio Santos ◽  
Helisson Oliveira Magalhaes Cerqueira ◽  
Hendrik Macedo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Model Compression ◽  
Deep Learning Model

scholarly journals Invoices Classification Using Deep Features Based on SME Perspectives

2021 ◽  
Author(s):  
Nurul Akmar Azman ◽  
Azlinah Mohamed ◽  
Amsyar Mohmad Jamil
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Learning Model ◽  
Discussion Section ◽  
Validation Data ◽  
Record Keeping ◽  
Small And Medium Enterprise ◽  
Lower Productivity ◽  
Medium Enterprise ◽  
Deep Learning Model

Abstract Automation is seen as a potential alternative in improving productivity in the twenty-first century. Invoicing is the essential foundation of accounting record keeping and serves as a critical foundation for law enforcement inspections by auditing agencies and tax authorities. With the rise of artificial intelligence, automated record keeping systems are becoming more widespread in major organizations, allowing them to do tasks in real time and with no effort as well as a decision-making tool. Despite the system's benefits, many small and medium-sized businesses, particularly in Malaysia, are hesitant to implement it. Invoices are mostly processed manually that prone to human errors and lower productivity of the company. Artificial intelligence will further improve automated invoice handling making it simpler and efficient for all levels of businesses especially the small and medium enterprise This study presents a deep learning approach on record keeping focusing on invoices recognition by detecting invoice image classification. The deep learning model used in this research including the classic architecture of Convolutional Neural Network and its other variation such as VGG-16, VGG-19 and ResNet-50. Besides that, the constrains and expectation of the system to be implemented in small and medium enterprise in Malaysia are also presented in the interview scores. The research highlighted a comparison result between deep learning model and the perspective of SME presented in the discussion section. ResNet-50 shows a significant value in both training and validation accuracy compared to the other models with 95.90% accuracy in training and 74.24% accuracy for validation data. Future work will look at the suggested other deep learning method and intelligence features to be implemented for a more efficient invoices recognition and for small and medium enterprise.

scholarly journals Voice Recognition and Inverse Kinematics Control for a Redundant Manipulator Based on a Multilayer Artificial Intelligence Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Mai Ngoc Anh ◽  
Duong Xuan Bien
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Inverse Kinematics ◽  
Degrees Of Freedom ◽  
Voice Recognition ◽  
Learning Model ◽  
Redundant Manipulator ◽  
Efficient Operation ◽  
Inverse Kinematics Problem ◽  
Deep Learning Model

This study presents the construction of a Vietnamese voice recognition module and inverse kinematics control of a redundant manipulator by using artificial intelligence algorithms. The first deep learning model is built to recognize and convert voice information into input signals of the inverse kinematics problem of a 6-degrees-of-freedom robotic manipulator. The inverse kinematics problem is solved based on the construction and training. The second deep learning model is built using the data determined from the mathematical model of the system’s geometrical structure, the limits of joint variables, and the workspace. The deep learning models are built in the PYTHON language. The efficient operation of the built deep learning networks demonstrates the reliability of the artificial intelligence algorithms and the applicability of the Vietnamese voice recognition module for various tasks.

Sign in / Sign up

Export Citation Format

Share Document