Bus Power Estimation and Power-Efficient Bus Arbitration for System-on-a-Chip Embedded Systems

Author(s):  
Ke Ning ◽  
David Kaeli
1997 ◽  
Vol 44 (1) ◽  
pp. 37-61 ◽  
Author(s):  
William Fornaciari ◽  
Paolo Gubian ◽  
Donatella Sciuto ◽  
Cristina Silvano

2007 ◽  
Vol E90-D (3) ◽  
pp. 676-679 ◽  
Author(s):  
M. JUN ◽  
K. BANG ◽  
H.-J. LEE ◽  
E.-Y. CHUNG

VLSI Design ◽  
2001 ◽  
Vol 12 (2) ◽  
pp. 139-150 ◽  
Author(s):  
Youngsoo Shin ◽  
Kiyoung Choi ◽  
Takayasu Sakurai

Power efficient design of real-time embedded systems based on programmable processors becomes more important as system functionality is increasingly realized through software. We address a power optimization method for real-time embedded applications on a variable speed processor. The method combines off-line and on-line components. The off-line component determines the lowest possible maximum processor speed while guaranteeing deadlines of all tasks. The on-line component dynamically varies the processor speed or bring a processor into a power-down mode to exploit execution time variations and idle intervals. Experimental results show that the proposed method obtains a significant power reduction across several kinds of applications.


2012 ◽  
Vol 605-607 ◽  
pp. 2049-2052
Author(s):  
Xiang Wen Liu ◽  
Li Min Liu

System on a chip, SoC, is an advanced technology of embedded systems. Mobile computing may support modern life style. Combination of SoC and mobile computing will produce a new product. In this paper, SoC, mobile computing and mobile computing SoC design are discussed. The mobile SoC can be used all mobile control fields, such as rescue, smart houses and mobile payment.


2020 ◽  
Author(s):  
Somdip Dey ◽  
Suman Saha ◽  
Amit Singh ◽  
Klaus D. Mcdonald-Maier

<div><div><div><p>Fruit and vegetable classification using Convolutional Neural Networks (CNNs) has become a popular application in the agricultural industry, however, to the best of our knowledge no previously recorded study has designed and evaluated such an application on a mobile platform. In this paper, we propose a power-efficient CNN model, FruitVegCNN, to perform classification of fruits and vegetables in a mobile multi-processor system-on-a-chip (MPSoC). We also evaluated the efficacy of FruitVegCNN compared to popular state-of-the-art CNN models in real mobile plat- forms (Huawei P20 Lite and Samsung Galaxy Note 9) and experimental results show the efficacy and power efficiency of our proposed CNN architecture.</p></div></div></div>


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