Swarm Intelligence for Digital Circuits Implementation on Field Programmable Gate Arrays Platforms

Field programmable gate arrays (FPGAs) are becoming increasingly important implementation platforms for digital circuits. One of the necessary requirements to effectively utilize the FPGA\u27s resources is an efficient placement and routing mechanism. This paper presents an optimization technique based on swarm intelligence for FPGA placement and routing. Mentor graphics technology mapping netlist file is used to generate initial FPGA placements and routings which are then optimized by particle swarm optimization (PSO). Results for the implementation of a binary coded decimal bidirectional counter and an arithmetic logic unit on a Xilinx FPGA show that PSO is a potential technique for solving the placement and routing problem

Comparison of Particle Swarm Optimization and Backpropagation as Training Algorithms for Neural Networks

Particle swarm optimization (PSO) motivated by the social behavior of organisms, is a step up to existing evolutionary algorithms for optimization of continuous nonlinear functions. Backpropagation (BP) is generally used for neural network training. Choosing a proper algorithm for training a neural network is very important. In this paper, a comparative study is made on the computational requirements of the PSO and BP as training algorithms for neural networks. Results are presented for a feedforward neural network learning a nonlinear function and these results show that the feedforward neural network weights converge faster with the PSO than with the BP algorithm

FPGA Placement and Routing Using Particle Swarm Optimization

Field programmable gate arrays (FPGAs) are becoming increasingly important implementation platforms for digital circuits. One of the necessary requirements to effectively utilize the FPGA\u27s fixed resources is an efficient placement and routing mechanism. This paper presents particle swarm optimization (PSO) for FPGA placement and routing. Preliminary results for the implementation of an arithmetic logic unit on a Xilinx FPGA show that PSO is a potential technique for solving the placement and routing problem

Optimal PSO for Collective Robotic Search Applications

Unmanned vehicles/mobile robots are of particular interest in target tracing applications since there are many areas where a human cannot explore. Different means of control have been investigated for unmanned vehicles with various algorithms like genetic algorithms, evolutionary computations, neural networks etc. This work presents the application of particle swarm optimization (PSO) for collective robotic search. The performance of the PSO algorithm depends on various parameters called quality factors and these parameters are determined using a secondary PSO. Results are presented to show that the performance of PSO algorithm and search is improved for a single and multiple target searches

Applications of particle swarm optimization for neural network training and digital systems

Particle Swarm Optimization (PSO) is an evolutionary computation technique similar to genetic algorithm, which is a population (swarm) based optimization tool. PSO starts with a population of random solutions called particles. Each particle is given a random velocity and is flown through the problem space. The particles work together to achieve a global task. The best particle of the entire swarm is taken as the final solution to the task. In this thesis, three problems are studied using the PSO; their results are presented, compared and contrasted with results obtained using conventional techniques. --Abstract, page iii