scholarly journals An OpenCores/Opensource based Embedded System-on-Chip Platform for Voice over Internet Protocol

10.5772/13385 ◽  
2011 ◽  
Author(s):  
Sabrina Titri ◽  
Nouma Izeboudjen ◽  
Fatiha Louiz ◽  
Mohamed Bakiri ◽  
Faroudja Abid ◽  
...  
Keyword(s):  
Embedded System ◽  
Internet Protocol ◽  
System On Chip ◽  
Voice Over Internet Protocol ◽  
On Chip

A Novel Design of Software System on Chip for Embedded System

2016 ◽  
Vol 86 (2-3) ◽  
pp. 135-147 ◽  
Author(s):  
Wei Hu ◽  
Hong Guo ◽  
Hongna Geng ◽  
Kai Zhang ◽  
Jun Liu ◽  
...  
Keyword(s):  
Embedded System ◽  
System On Chip ◽  
Software System ◽  
On Chip ◽  
Novel Design

A Novel Operating System on Chip with Information Security Support for Embedded System

2009 ◽  
Vol 4 (10) ◽  
Author(s):  
Wei Hu ◽  
Tianzhou Chen ◽  
Qingsong Shi ◽  
Gang Wang ◽  
Nan Zhang ◽  
...  
Keyword(s):  
Operating System ◽  
Information Security ◽  
Embedded System ◽  
System On Chip ◽  
On Chip

scholarly journals Hardware-software partitioning using three-level hybrid algorithm for system-on-chip platform

2021 ◽  
Vol 10 (1) ◽  
pp. 466-473
Author(s):  
Tiong Reng Xian ◽  
Zaini Abdul Halim ◽  
Ching Chia Leong ◽  
Tan Jiunn Gim
Keyword(s):  
Embedded System ◽  
Hybrid Algorithm ◽  
High Capacity ◽  
Minimum Cost ◽  
System On Chip ◽  
Swarm Optimization ◽  
Low Diversity ◽  
Field Programmable ◽  
On Chip ◽  
Software Partitioning

This study discusses hardware-software partitioning, which is useful for system-on-chip (SoC) applications. Hardware-software partitioning attempts to obtain the lowest execution time by combining a hardware processor system and a field programmable gate array on the SoC platform in embedded system applications. A three-level hybrid algorithm called GAGAPSO is proposed in this study. The algorithm consists of two successive genetic algorithms (GAs) and one particle swarm optimization (PSO). The drawbacks of these two algorithms are GA has low convergence speed and PSO has premature convergence because of low diversity. These algorithms are combined in this study to achieve high-capacity global convergence and enhanced search efficiency. In this study, three algorithms are developed, namely, GA, GAPSO and GAGAPSO using MATLAB. These algorithms are evaluated on the basis of the number of nodes and the minimum cost that can be achieved. The number of nodes varies from 10 to 1000 nodes. The minimum cost and the number of iterations to achieve the minimum cost are recorded. Results show that GAGAPSO can converge faster than GA and GAPSO. Furthermore, GAGAPSO can achieve the lowest cost for all nodes. 

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