粒子群优化中基于自适应区域编码的解耦电容大规模优化

IF 1.8 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY IEEE Open Journal of Nanotechnology Pub Date : 2022-11-23 DOI:10.1109/OJNANO.2022.3224061
DINESH JUNJARIYA;JAI NARAYAN TRIPATHI
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引用次数: 0

摘要

电力输送网络负责向集成电路提供清洁电力。电源噪声对高速超大规模集成电路和系统的性能起着至关重要的作用。为了在高速系统中保持电源的完整性,去耦电容器被用于维持PDN的低阻抗,最终使电源噪声最小化。然而,在具有严格功率完整性(PI)要求的系统中,选择去耦电容的离散优化问题在计算上具有挑战性。在这项工作中,使用社会学习粒子群优化(SLPSO)技术和自适应区域搜索(ARS)来解决解耦电容器放置的大规模优化问题(LSOP)。利用区域搜索(Region Search, RS)引导粒子动态搜索局部最优位置,使粒子在保持种群多样性的前提下更快地在搜索空间中移动。为了证明所提出的方法,提出了三个实际案例研究。所得结果与目前最先进的方法进行了比较。该方法大大减少了计算时间,并且与其他方法相比具有更好的一致性。在所有示例的结果中,CPU时间改善的一致性验证了所提出的方法。
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Large-Scale Optimization of Decoupling Capacitors Using Adaptive Region Based Encoding Scheme in Particle Swarm Optimization
Power delivery networks are responsible for supplying clean power to the integrated circuits. Power supply noise plays a critical role in determining the performance of high-speed very large scale integration circuits and systems. In order to maintain power integrity in high-speed systems, decoupling capacitors are used to maintain low impedance of the PDN to eventually minimize power supply noise. However, the discrete optimization problem of selecting decoupling capacitors becomes computationally challenging in the systems having stringent power integrity (PI) requirements. In this work, a novel approach using the Social-Learning Particle Swarm Optimization (SLPSO) technique along with Adaptive Region Search (ARS) is used to tackle the Large-Scale Optimization Problem (LSOP) of decoupling capacitor placement. Region Search (RS) is used to guide particles, followed by ARS to dynamical search for the local best positions and for particles to move faster across the search space while maintaining the diversity of the population. To demonstrate the proposed approach, three practical case studies are presented. The obtained results are compared with current state-of-the-art approaches. The proposed approach drastically reduces computation time and is consistent with better results than other approaches. This consistency of improvement in CPU time in the results of all the examples validates the proposed approach.
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来源期刊
CiteScore
3.90
自引率
17.60%
发文量
10
审稿时长
12 weeks
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