: Subhrapratim Nath, Avik Kumar Chakravarty, Sudipta Ghosh,
Subir Kumar Sarkar
: 1Departments of a,bCSE & cECE, Meghnad Saha Institute of Technology, Kolkata 700150, India
2Electronics & Telecommunication Engineering, Jadavpur University, Kolkata 700032, India
Field Programmable Gate Array (FPGA) technology has been surging in popularity across all industries. Digital circuit design using FPGA provides many benefits over previous methods like ASIC implementation. However, the fixed hardware structure of FPGA demands an efficient placement and routing technique for high-performance goals. It has already been demonstrated that metaheuristic algorithms like Particle Swarm Optimization (PSO) can successfully optimize circuit designs for the FPGA placement and routing problem. The limitation of PSO lies in the explosion of particle swarms due to unbounded exploration. This paper proposes the application of Firefly Algorithm (FA) as an alternative and competitive metaheuristic solution for FPGA placement optimization. The design optimization of a single BCD counter circuit is performed by applying the proposed algorithm on Xilinx software generated netlist and the simulation result is compared with the existing PSO based placement techniques aiming at better placement optimization and utilization of FPGA resources.
(size: 406.33 kB, 97-102
, Download times:
P. Maidee, C. Ababei and K. Bazargan, 2005. Timing-driven partitioning-based placement for island style FPGAs, IEEE Trans. Computer-Aided Design of Integrated Circuits and Systems, vol. 24, no. 3, pp. 395-406.
Y. Xu and M. A. S. Khalid, 2005. QPF: efficient quadratic placement for FPGAs, Int’l Conf. Field Programmable Logic and Applications, pp. 555-558.
X. Guo, T. Wang, Z. Chen, L. Wang and W. Zhao, 2009. Fast FPGA placement algorithm using Quantum Genetic Algorithm with Simulated Annealing, IEEE 8th Int’l Conf. ASIC, pp. 730-733.
M. Yang, A. E. Almaini and L. Wang, 2007. FPGA placement by using a genetic algorithm, EngineerIT, pp. 50-53.
K. Wang and N. Xu, 2009, Ant Colony Optimization for symmetrical FPGA placement, IEEE 11th Int’l Conf. Computer-Aided Design and Computer Graphics, pp. 561-563.
G. K. Venayagamoorthy and V. G. Gudise, 2004. Swarm intelligence for digital circuits implementation on field programmable gate arrays platforms, NASA/DoD Conf. Evolvable Hardware, pp. 83-86.
M. El-Abd, H. Hassan and M. S. Kamel, 2009. Discrete and continuous particle swarm optimization for FPGA placement, IEEE Cong. Evolutionary Computation, pp. 706-711.
P. K. Rout, D. P. Acharya and G. Panda, 2010. Novel PSO based FPGA Placement Techniques, Int’l Conf. on Computer & Communication Technology, pp. 630-634.
X. S. Yang, 2009. Firefly Algorithms for Multimodal Optimization, 5th Int’l Symp. Stochastic Algorithms, Foundations and Applications, pp. 169-178.
S. Yang, S. S. S. Hosseini and A. H. Gandomi, 2012, Firefly Algorithm for solving non-convex economic dispatch problems with valve loading effect, Applied Soft Computing, vol. 12, no. 3, pp. 1180-1186.
Sh. M. Farahani, A. A. Abshouri, B. Nasiri and M. R. Meybodi, 2011, A Gaussian Firefly Algorithm, Int’l Journal of Machine Learning and Computing, vol. 1, issue 5, pp. 448-453.
X. S. Yang, 2011, Chaos Enhanced Firefly Algorithm with Automatic Parameter Tuning, Int’l Journal of Swarm Intelligence Research, vol. 4, issue 2, pp. 1-11.