A Radial Basis Function Network-Based Surrogate-Assisted Swarm Intelligence Approach for Fast Optimization of Power Delivery Networks

Abstract

The design and optimization of power delivery networks (PDNs) in very large scale integration systems are becoming very challenging with the increasing complexity of such systems. Decoupling capacitors are the key elements used in a PDN to minimize power supply noise and to maintain low impedance of the PDN to avoid system failure. In this article, a novel approach using surrogate-assisted swarm intelligence is presented for efficient and fast optimization of PDNs. For generating the surrogate models, a standard radial basis function network is used. Using the proposed approach, the decoupling capacitors are selected and placed optimally, eventually reducing the cumulative impedance of the PDN below the target impedance. The performance comparison between the conventional and the surrogate-assisted approach is presented. Three case studies are presented on a practical system to demonstrate the competence of the proposed approach. The results obtained by the proposed approach are also compared with the same obtained by the state-of-the-art approaches. For the proposed approach, the runtime is drastically reduced compared to the state-of-the-art approaches for the optimization problem without having any effect on the performance. The consistency of results in all of the case studies confirms the validity of the proposed approach.