MO-PSE: Adaptive multi-objective particle swarm optimization based design space exploration in architectural synthesis for application specific processor design

• Novel DSE methodology employing PSO for multi-objective trade-off was proposed.
• A novel fitness function for solution assessment based on power parameter was introduced.
• A novel mutation algorithm for improving DSE convergence and exploration time was presented.
• Novel perturbation algorithm to handle boundary outreach problem during DSE was presented.
• Average improvement in QoR ∼ 9% and reduction in time > 90% was obtained compared to recent GA DSE.

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.