Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Optimised Meta-heuristic Queuing Model In Vlsi Physical Design


Affiliations
1 Department of Mathematics, St. Xavier’s Catholic College of Engineering, India
2 Department of Computer Science and Engineering, IES College of Engineering, India
3 College of Computer Science and Information Science, Srinivas University, India
     

   Subscribe/Renew Journal


In this paper, the Markov M/D/1/B queuing model that has been created will be discussed. One approach to evaluating the effectiveness of a router is to use a model of a queue that is analogous to the one that we presented above. Having said that, this can be accomplished in a variety of different ways. According to the results of the studies, altering the service rate has a discernible bearing not only on the amount of data the system processes but also on the effectiveness of its operation. For the purpose of demonstrating the viability of our methodology, we built an output-queuing router using FPGA. When utilising this method, it is possible to observe that the queues and the control unit of the router take up an excessive amount of space on the silicon. This is a consequence of the fact that this method is utilised. The difference in effectiveness between a theoretical model and a real prototype is only 2%. This is a very small margin. The study implemented our design on a Xilinx FPGA Vertix II Pro family 100K chips. In the following subsections, we present the router FPGA synthesis results and evaluate its performance.

Keywords

Antenna, Equivalent Circuit Modelling, RLC Circuit, Series Resonance, Q Factor, Bandwidth
Subscription Login to verify subscription
User
Notifications
Font Size

  • Ikeguchi, T., & Aihara, K. (2008). Meta-Heuristic Algorithms with Chaotic Neuro-Dynamics for Solving Combinatorial Optimization Problems. IEICE Proceedings Series, 42(A3L-F3).
  • Alagarsamy, A., & Gopalakrishnan, L. (2016, May). SAT: A new application mapping method for power optimization in 2D—NoC. In 2016 20th International Symposium on VLSI Design and Test (VDAT) (pp. 1-6). IEEE.
  • Areibi, S., & Vannelli, A. (2000). Tabu search: A meta heuristic for netlist partitioning. VLSI Design, 11(3), 259-283.
  • Gonsalves, T., & Itoh, K. (2006). Simulated Annealing In The Optimization Of Collaborative Systems Operation. Journal of Integrated Design and Process Science, 10(3), 87-95.
  • Mao, F., Xu, N., & Ma, Y. (2009, November). Hybrid algorithm for floorplanning using B*-tree representation. In 2009 Third International Symposium on Intelligent Information Technology Application (Vol. 3, pp. 228-231). IEEE.
  • Emmert, J. M., Lodha, S., & Bhatia, D. K. (2003). On using tabu search for design automation of VLSI systems. Journal of Heuristics, 9(1), 75-90.
  • Priyadarshini, R., Barik, R. K., & Mishra, B. K. (2020). Meta-Heuristic and Non-Meta-Heuristic Energy-Efficient Load Balancing Algorithms in Cloud Computing. In Modern principles, practices, and algorithms for cloud security (pp. 203-222). IGI Global.
  • Rajesh, K., & Pyne, S. (2021). Invasive weed optimization based scheduling for digital microfluidic biochip operations. Integration, 76, 122-134.
  • Wang, R., & Lu, J. (2021). QoS-Aware Service Discovery and Selection Management for Cloud-Edge Computing Using a Hybrid Meta-Heuristic Algorithm in IoT. Wireless Personal Communications, 1-14.
  • Cao, K., Cui, Y., Liu, Z., Tan, W., & Weng, J. (2021). Edge intelligent joint optimization for lifetime and latency in large-scale cyber-physical systems. IEEE Internet of Things Journal.
  • Xie, G., Xiao, X., Peng, H., Li, R., & Li, K. (2021). A survey of low-energy parallel scheduling algorithms. IEEE Transactions on Sustainable Computing.
  • Tariq, U. U., Ali, H., Liu, L., Panneerselvam, J., & Zhai, X. (2019). Energy-efficient static task scheduling on VFI-based NoC-HMPSoCs for intelligent edge devices in cyber-physical systems. ACM Transactions on Intelligent Systems and Technology (TIST), 10(6), 1-22.
  • Jayabalan, M., Srinivas, E., Shajin, F. H., & Rajesh, P. (2021). On Reducing Test Data Volume for Circular Scan Architecture Using Modified Shuffled Shepherd Optimization. Journal of Electronic Testing, 1-16.
  • Cao, K., Zhou, J., Wei, T., Chen, M., Hu, S., & Li, K. (2019). A survey of optimization techniques for thermal-aware 3D processors. Journal of Systems Architecture, 97, 397-415.
  • Kumar, R., & Banerjee, N. (2011). Multiobjective network topology design. Applied Soft Computing, 11(8), 5120-5128.
  • Youssef, H., Sait, S. M., & Ali, H. (2003). Fuzzy simulated evolution algorithm for VLSI cell placement. Computers & Industrial Engineering, 44(2), 227-247.
  • Amiri-Zarandi, M., Safaei, F., & Roozikhar, M. (2015). Performance evaluation of generic multi-stage interconnection networks with blocking and back-pressure mechanism. The Journal of Supercomputing, 71(3), 1038-1066.
  • Rodrigues, D., Souza, A. N., & Papa, J. P. (2017, October). Pruning Optimum-Path Forest Classifiers Using Multi-Objective Optimization. In 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) (pp. 127-133). IEEE.
  • Torkzadeh, S., Soltanizadeh, H., & Orouji, A. A. (2021). Energy-aware routing considering load balancing for SDN: a minimum graph-based Ant Colony Optimization. Cluster Computing, 24(3), 2293-2312.
  • Sadatdiynov, K., Cui, L., Zhang, L., Huang, J. Z., Salloum, S., & Mahmud, M. S. (2022). A review of optimization methods for computation offloading in edge computing networks. Digital Communications and Networks.
  • Giagkos, A., & Wilson, M. S. (2014). BeeIP–A Swarm Intelligence based routing for wireless ad hoc networks. Information Sciences, 265, 23-35.
  • Youssef, H., Sait, S. M., & Khan, S. A. (2001, March). Fuzzy evolutionary hybrid metaheuristic for network topology design. In International Conference on Evolutionary Multi-Criterion Optimization (pp. 400-415). Springer, Berlin, Heidelberg.
  • Lodha, S. K., & Bhatia, D. (1998, September). Bipartitioning circuits using TABU search. In Proceedings Eleventh Annual IEEE International ASIC Conference (Cat. No. 98TH8372) (pp. 223-227). IEEE.
  • Ali, H., Tariq, U. U., Hardy, J., Zhai, X., Lu, L., Zheng, Y., ... & Antonopoulos, N. (2021). A survey on system level energy optimisation for MPSoCs in IoT and consumer electronics. Computer Science Review, 41, 100416.
  • Nguyen, V. T. N., & Kirner, R. (2015). Throughput-driven partitioning of stream programs on heterogeneous distributed systems. IEEE Transactions on Parallel and Distributed Systems, 27(3), 913-926.
  • Chaudhry, R., Tapaswi, S., & Kumar, N. (2019). A green multicast routing algorithm for smart sensor networks in disaster management. IEEE Transactions on Green Communications and Networking, 3(1), 215-226.
  • Chaudhry, R., Tapaswi, S., & Kumar, N. (2019). A green multicast routing algorithm for smart sensor networks in disaster management. IEEE Transactions on Green Communications and Networking, 3(1), 215-226.
  • Furuholmen, M., Glette, K., Hovin, M., & Torresen, J. (2010, July). A Coevolutionary, Hyper Heuristic approach to the optimization of Three-dimensional Process Plant Layouts—A comparative study. In IEEE Congress on Evolutionary Computation (pp. 1-8). IEEE.
  • Siddavaatam, P. (2018). A Delta Diagram Synthesis for IoT Optimization with Grey Wolf Driven Multi-Objective Automation (Doctoral dissertation, Ryerson University).
  • Sahu, P. K., & Chattopadhyay, S. (2013). A survey on application mapping strategies for network-on-chip design. Journal of systems architecture, 59(1), 60-76

Abstract Views: 129

PDF Views: 0




  • Optimised Meta-heuristic Queuing Model In Vlsi Physical Design

Abstract Views: 129  |  PDF Views: 0

Authors

L. Mary Florida
Department of Mathematics, St. Xavier’s Catholic College of Engineering, India
S. Brilly Sangeetha
Department of Computer Science and Engineering, IES College of Engineering, India
K. Krishna Prasad
College of Computer Science and Information Science, Srinivas University, India

Abstract


In this paper, the Markov M/D/1/B queuing model that has been created will be discussed. One approach to evaluating the effectiveness of a router is to use a model of a queue that is analogous to the one that we presented above. Having said that, this can be accomplished in a variety of different ways. According to the results of the studies, altering the service rate has a discernible bearing not only on the amount of data the system processes but also on the effectiveness of its operation. For the purpose of demonstrating the viability of our methodology, we built an output-queuing router using FPGA. When utilising this method, it is possible to observe that the queues and the control unit of the router take up an excessive amount of space on the silicon. This is a consequence of the fact that this method is utilised. The difference in effectiveness between a theoretical model and a real prototype is only 2%. This is a very small margin. The study implemented our design on a Xilinx FPGA Vertix II Pro family 100K chips. In the following subsections, we present the router FPGA synthesis results and evaluate its performance.

Keywords


Antenna, Equivalent Circuit Modelling, RLC Circuit, Series Resonance, Q Factor, Bandwidth

References