Exploring Direct Convolution Performance on the Gemmini Accelerator

Convolutional Neural Network (CNN) algorithms are becoming a recurrent solution to solve Computer Vision related problems. These networks employ convolutions as main building block, which greatly impact their performance since convolution is a costly operation. Due to its importance in CNN algorithms, this work evaluates convolution performance in the Gemmini accelerator and compare it to a conventional lightlyand heavily-loaded desktop CPU in terms of execution time and energy consumption. We show that Gemmini can achieve lower execution time and energy consumption when compared to a CPU even for small convolutions, and this performance gap grows with convolution size. Furthermore, we analyze the minimum Gemmini required frequency to match the same CPU execution time, and show that Gemmini can achieve the same runtime while working in much lower frequencies.

Download Full-text

Estimation of energy consumption of electric vehicles using Deep Convolutional Neural Network to reduce driver’s range anxiety

ISA Transactions ◽

10.1016/j.isatra.2019.08.055 ◽

2020 ◽

Vol 98 ◽

pp. 454-470 ◽

Cited By ~ 1

Author(s):

Shatrughan Modi ◽

Jhilik Bhattacharya ◽

Prasenjit Basak

Keyword(s):

Neural Network ◽

Energy Consumption ◽

Convolutional Neural Network ◽

Electric Vehicles ◽

Deep Convolutional Neural Network ◽

Range Anxiety

Download Full-text

A Computer Vision System for the Categorization of Citrus Fruits Using Convolutional Neural Network

2021 International Symposium on Electronics and Smart Devices (ISESD) ◽

10.1109/isesd53023.2021.9501790 ◽

2021 ◽

Author(s):

Md. Mehedi Hasan ◽

Imrus Salehin ◽

Nazmun Nessa Moon ◽

T. M. Kamruzzaman ◽

Baki-Ul-Islam ◽

...

Keyword(s):

Neural Network ◽

Computer Vision ◽

Convolutional Neural Network ◽

Vision System ◽

Citrus Fruits ◽

Computer Vision System

Download Full-text

Joint Optimization Offloading Strategy of Execution Time and Energy Consumption of Mobile Edge Computing

The International Arab Journal of Information Technology ◽

10.34028/iajit/18/5/114 ◽

2021 ◽

Vol 18 (5) ◽

Author(s):

Qingzhu Wang ◽

Xiaoyun Cui

Keyword(s):

Energy Consumption ◽

Mobile Devices ◽

Execution Time ◽

Optimal Solution ◽

Computation Offloading ◽

Joint Optimization ◽

Random Strategy ◽

Fitness Value ◽

Cloud Server ◽

Time And Energy

As mobile devices become more and more powerful, applications generate a large number of computing tasks, and mobile devices themselves cannot meet the needs of users. This article proposes a computation offloading model in which execution units including mobile devices, edge server, and cloud server. Previous studies on joint optimization only considered tasks execution time and the energy consumption of mobile devices, and ignored the energy consumption of edge and cloud server. However, edge server and cloud server energy consumption have a significant impact on the final offloading decision. This paper comprehensively considers execution time and energy consumption of three execution units, and formulates task offloading decision as a single-objective optimization problem. Genetic algorithm with elitism preservation and random strategy is adopted to obtain optimal solution of the problem. At last, simulation experiments show that the proposed computation offloading model has lower fitness value compared with other computation offloading models.

Download Full-text

Convolutional Neural Network Model in Machine Learning Methods and Computer Vision for Image Recognition: A Review

Journal of Applied Sciences Research ◽

10.22587/jasr.2018.14.6.5 ◽

2018 ◽

Keyword(s):

Neural Network ◽

Machine Learning ◽

Computer Vision ◽

Convolutional Neural Network ◽

Network Model ◽

Image Recognition ◽

Neural Network Model ◽

Learning Methods ◽

Machine Learning Methods

Download Full-text

AlexNet convolutional neural network to classify the types of Indonesian coffee beans

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/905/1/012059 ◽

2021 ◽

Vol 905 (1) ◽

pp. 012059

Author(s):

Y Hendrawan ◽

B Rohmatulloh ◽

F I Ilmi ◽

M R Fauzy ◽

R Damayanti ◽

...

Keyword(s):

Neural Network ◽

Computer Vision ◽

Sensitivity Analysis ◽

Convolutional Neural Network ◽

Confusion Matrix ◽

Learning Rate ◽

Agricultural Products ◽

Coffee Bean ◽

Coffee Beans ◽

Non Destructive

Abstract Various types of Indonesian coffee are already popular internationally. Recently, there are still not many methods to classify the types of typical Indonesian coffee. Computer vision is a non-destructive method for classifying agricultural products. This study aimed to classify three types of Indonesian Arabica coffee beans, i.e., Gayo Aceh, Kintamani Bali, and Toraja Tongkonan, using computer vision. The classification method used was the AlexNet convolutional neural network with sensitivity analysis using several variations of the optimizer such as SGDm, Adam, and RMSProp and the learning rate of 0.00005 and 0.0001. Each type of coffee used 500 data for training and validation with the distribution of 70% training and 30% validation. The results showed that all AlexNet models achieved a perfect validation accuracy value of 100% in 1,040 iterations. This study also used 100 testing-set data on each type of coffee bean. In the testing confusion matrix, the accuracy reached 99.6%.

Download Full-text