A Multi-Objective Evolutionary Algorithm Based Optimization Model for Network-on-Chip Synthesis

2007 ◽  
Author(s):  
Rabindra Ku. Jena ◽  
Gopal Ku. Sharma
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Model ◽  
Network On Chip ◽  
Multi Objective ◽  
On Chip

scholarly journals Contention-Aware Mapping and Scheduling Optimization for NoC-Based MPSoCs (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13995-13996
Author(s):  
Yupeng Zhou ◽  
Rongjie Yan ◽  
Anyu Cai ◽  
Yige Yan ◽  
Minghao Yin
Keyword(s):  
Evolutionary Algorithm ◽  
Network On Chip ◽  
Multi Objective Optimization ◽  
Scheduling Optimization ◽  
Multi Objective ◽  
Temporal Aspects ◽  
Network Contention ◽  
On Chip ◽  
Constraint Model

We consider spacial and temporal aspects of communication to avoid contention in Network-on-Chip (NoC) architectures. A constraint model is constructed such that the design concerns can be evaluated, and an efficient evolutionary algorithm with various heuristics is proposed to search for better solutions. Experimentations from random benchmarks demonstrate the efficiency of our method in multi-objective optimization and the effectiveness of our techniques in avoiding network contention.

scholarly journals Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments

2015 ◽  
Vol 18 (6) ◽  
pp. 1737-1757 ◽  
Author(s):  
Fahimeh Ramezani ◽  
Jie Lu ◽  
Javid Taheri ◽  
Farookh Khadeer Hussain
Keyword(s):  
Evolutionary Algorithm ◽  
Task Scheduling ◽  
Optimization Model ◽  
Scheduling Optimization ◽  
Multi Objective ◽  
Cloud Environments

scholarly journals Parallel Applications Mapping onto Network on Chip Based on Heterogeneous MPSoCs Using Hybrid Algorithms

2019 ◽  
Vol 10 (2) ◽  
pp. 37-63
Author(s):  
Dihia Belkacemi ◽  
Mehammed Daoui ◽  
Samia Bouzefrane ◽  
Youcef Bouchebaba
Keyword(s):  
Energy Consumption ◽  
Population Based ◽  
Network On Chip ◽  
Hybrid Algorithms ◽  
Parallel Applications ◽  
High Quality ◽  
Multi Objective ◽  
Hybrid Solutions ◽  
Single Solution ◽  
On Chip

Mapping parallel applications onto a network on chip (NoC) that is based on heterogeneous MPSoCs is considered as an instance of an NP-hard and a multi-objective problem. Various multi-objective algorithms have been proposed in the literature to handle this issue. Metaheuristics stand out as highly appropriate approaches to deal with this kind of problem. These metaheuristics are classified into two sets: population-based metaheuristics and single solution-based ones. To take advantage of the both sets, the trend is to use hybrid solutions that have shown to give better results. In this article, the authors propose to hybridize these two metaheuristics sets to find good Pareto mapping solutions to optimize the execution time and the energy consumption simultaneously. The experimental results have shown that the proposed hybrid algorithms give high quality non-dominated mapping solutions in a reasonable runtime.

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