HLS driven Hybrid GA-PSO for Design Space Exploration of Optimal Palmprint Biometric based IP Watermark and Loop Unrolling Factor

Abstract

This paper proposes a novel high level synthesis (HLS) based hybrid Genetic Algorithm-Particle Swarm Optimization (GA-PSO) framework for concurrent design space exploration (DSE) of optimal palmprint biometric based intellectual property (IP) watermark and optimal loop unrolling factor. The proposed approach is a hybrid exploration framework that merges GA process with specific utility attributes of PSO based DSE framework such as mutation, local and global best design solution concepts, which aids in escaping getting stuck at local minima. The proposed approach is capable of providing low-cost robust detective security countermeasure against threats of IP ownership abuse and IP piracy using IP seller's optimal palmprint biometric watermark. The comparison of the proposed approach with related works demonstrated optimal quality palmprint watermark in terms of lower probability of coincidence (watermark collision) and stronger tamper tolerance ability, at zero design cost overhead. © 2024 IEEE.