An Optimized Nature-Inspired Metaheuristic Algorithm for Application Mapping in 2D-NoC

Sensors

Saleha Sikandar¹  Waqar Amin¹  Naveed Khan Baloch¹  Fawad Hussain¹  Heejung Yu²  Yousaf Bin Zikria³ 

[1] Computer Engineering Department, University of Engineering and Technology, Taxila 47050, Pakistan;Department of Electronics and Information Engineering, Korea University, Sejong 30019, Korea;Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea;

关键词: network-on-chip;    sailfish hunting;    metaheuristic optimization;   

DOI  :  10.3390/s21155102

来源: DOAJ

【 摘 要 】

Mapping application task graphs on intellectual property (IP) cores into network-on-chip (NoC) is a non-deterministic polynomial-time hard problem. The evolution of network performance mainly depends on an effective and efficient mapping technique and the optimization of performance and cost metrics. These metrics mainly include power, reliability, area, thermal distribution and delay. A state-of-the-art mapping technique for NoC is introduced with the name of sailfish optimization algorithm (SFOA). The proposed algorithm minimizes the power dissipation of NoC via an empirical base applying a shared k-nearest neighbor clustering approach, and it gives quicker mapping over six considered standard benchmarks. The experimental results indicate that the proposed techniques outperform other existing nature-inspired metaheuristic approaches, especially in large application task graphs.

【 授权许可】

Unknown