Architectural synthesis has gained rapid dominance in the design flows of application specific computing. Exploring an optimal design point during architectural synthesis is a tedious task owing to the orthogonal issues of reducing exploration time and enhancing design quality as well as resolving the conflicting parameters of power and performance. This paper presents a novel design space exploration (DSE) methodology multi-objective particle swarm exploration MO-PSE, based on the particle swarm optimization (PSO) for designing application specific processor (ASP). To the best of the authors’ knowledge, this is the first work that directly maps a complete PSO process for multi-objective DSE for power-performance trade-off of application specific processors. Therefore, the major contributions of the paper are: (i) Novel DSE methodology employing a particle swarm optimization process for multi-objective tradeoff, (ii) Introduction of a novel model for power parameter used during evaluation of design points in MO-PSE, (iii) A novel fitness function used for design quality assessment, (iv) A novel mutation algorithm for improving DSE convergence and exploration time, (v) Novel perturbation algorithm to handle boundary outreach problem during exploration and (vi) Results of comparison performed during multiple experiments that indicates average improvement in the quality of results (QoR) achieved is around 9% and average reduction in exploration time of greater than 90% compared to recent genetic algorithm (GA) based DSE approaches. The paper also reports results based on the variation and impact of different PSO parameters such as swarm size, inertia weight, acceleration coefficient, and termination condition on multi-objective DSE. multi-objective particle swarm exploration design space exploration high level synthesis particle swarm optimization system on chip lower boundary of design space upper boundary of design space resource configuration resource configuration (position) of ith particle dth dimension resource configuration (position) of ith particle new resource configuration (position) of ith particle dth dimension local best resource configuration of ith particle global best resource configuration local best resource configuration velocity velocity of ith particle dth dimension new velocity of ith particle dth dimension fitness function local best fitness of ith particle global best fitness among all particle number of Iteration current iteration number of particle (in Figs. 1 and 2 n =Ns) number of dimension current dimension minimum power consumed by resource configuration power consumption constrained specified by user maximum power consumed by resource c configuration total power consumed by resource configuration minimum execution time taken by a resource configuration execution time constrained specified by user maximum execution time taken by a resource configuration execution time taken by a resource configuration stopping criterion 
MO-PSE: Adaptive multi-objective particle swarm optimization based design space exploration in architectural synthesis for application specific processor design