Adaptive Iterative Improvement GP-based Methodology for HW/SW Co-synthesis of Embedded Systems

被引：0

作者：

Gorski, Adam ^{[1
]}

Ogorzalek, Maciej ^{[1
]}

机构：

[1] Jagiellonian Univ Cracow, Dept Informat Technol, Prof Stanislawa Lojasiewicza 11, Krakow, Poland

来源：

PROCEEDINGS OF THE 7TH INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND EMBEDDED COMPUTING AND COMMUNICATION SYSTEMS (PECCS) | 2017年

关键词：

Embedded Systems; Architecture; Hardware/Software Co-synthesis; Genetic Programming; Genetic Algorithm; Adaptive Systems; GENETIC ALGORITHM;

D O I：

10.5220/0006476500560059

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The paper presents a novel adaptive genetic programming based iterative improvement algorithm for hardware/software co-synthesis of distributed embedded systems. The algorithm builds solutions by starting from suboptimal architecture (the fastest) and using system-building options improves the system's quality. Most known genetic programming algorithms for co-synthesis of embedded systems are built choosing fixed probability. In our approach we decided to change the probability during the work of the program.

引用

页码：56 / 59

页数：4

共 11 条

[1] GP-based Methodology for HW/SW Co-synthesis of Multiprocessor Embedded Systems with Increasing Number of Individuals Obtained by Mutation
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PECCS 2015 PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON PERVASIVE AND EMBEDDED COMPUTING AND COMMUNICATION SYSTEMS, 2015, : 275 - 280
[2] Genetic Programming based Iterative Improvement Algorithm for HW/SW Cosynthesis of Distributted Embedded Systems
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[4] Genetic Programming based Algorithm for HW/SW Cosynthesis of Distributed Embedded Systems Specified using Conditional Task Graph
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[8] COFTA: Hardware-software co-synthesis of heterogeneous distributed embedded systems for low overhead fault tolerance
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[9] Co-synthesis of contention-free energy-efficient NOC-based real time embedded systems
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[10] Hardware-software co-synthesis of partially re-configurable embedded systems optimized for reduced power consumption
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