Fast Algorithm for Minimizing the Number of decap in Power Distribution Networks

Koo, Kyoungchoul; Luevano, Gerardo Romo; Wang, Tao; Ozbayat, Selman; Michalka, Tim; Drewniak, James L.

doi:10.1109/temc.2017.2746677

Cited by 26 publications

(13 citation statements)

References 7 publications

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“…2) Sorting Search Algorithm [16]: In this algorithm, the relative importance of the decap ports and the different decap types are first decided. Based on some empirical knowledge and the relative importance of the decap locations and decap types, a complex decision-making procedure is proposed to add decaps sequentially to the candidate locations.…”

Section: B Conventional Optimization Methodsmentioning

confidence: 99%

“…According to physical understanding, decap locations closer to the IC are more effective as they provide current return paths with smaller loop inductances. Therefore, the method proposed in [12], [16] is used to quantitatively evaluate the priority of each decap location.…”

Section: A Port Prioritization Based On Physical Inductancementioning

confidence: 99%

“…Recently, with the popularization of artificial intelligence, machine learning (ML)-based methods, such as reinforcement learning (RL) [11], [12], [13], [14], have been broadly adopted in PI optimization problems. Besides, some algorithms based on human experience and knowledge have also been proposed to quickly determine the decap distribution, such as the Newton-Hessian minimization method [15] and several other approaches [16], [17], [18], [19], [20] with different empirical knowledge and decision-making rules.…”

Section: Introductionmentioning

confidence: 99%

“…[11], [12], [13], [14] require a significant amount of time for data generation and model training, and the robustness and generalization performance of the RL models are difficult to ensure. The human-knowledge-inspired methods [15], [16], [17], [18], [19], [20] can find feasible decap solutions, but the solution quality cannot be guaranteed for large-scale scenarios. Besides, some methods mentioned above [13], [20] can only optimize either the selection of decap types or the decap locations, but not simultaneously.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Physics-Assisted Genetic Algorithm for Decoupling Capacitor Optimization

Jiang,

Zhang,

Tan

et al. 2024

IEEE Trans. Microwave Theory Techn.

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This article proposes a new physics-assisted genetic algorithm (PAGA) for decoupling capacitor (decap) optimization in power distribution networks (PDNs), which is, by far, the most powerful approach to efficiently minimizing the number of decaps within an enormous search space. In the proposed PAGA method, the priority of the decap ports is first determined based on their physical loop inductances. Then, an initial solution is quickly obtained by placing decaps sequentially on the port with the highest priority. Subsequently, a GA with prior physical knowledge is developed to find better decap solutions progressively. A port removal scheme that eliminates the lowpriority ports and a modified mutation operation that better guides the mutation direction is proposed to accelerate the convergence of the GA. The initial solution and the proposed auxiliary schemes can significantly narrow the search space and incorporate physical knowledge into the GA, thus greatly accelerating the convergence process. Several representative stateof-the-art algorithms and commercial tools are thoroughly compared with the proposed PAGA in different application scenarios. The proposed PAGA shows remarkably better performance in efficiently finding high-quality decap solutions and exhibits strong robustness to handle real-world and largescale problems, which brings decap optimization algorithms to a new benchmark.

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Section: B Conventional Optimization Methodsmentioning

confidence: 99%

Section: A Port Prioritization Based On Physical Inductancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Physics-Assisted Genetic Algorithm for Decoupling Capacitor Optimization

Jiang,

Zhang,

Tan

et al. 2024

IEEE Trans. Microwave Theory Techn.

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“…Many previous work exists on optimizing the placement, sizing, and selection of decoupling capacitors on a PDN [6]- [9]. However, these work are based on theories and rule of thumbs which can vary from design…”

Section: Introductionmentioning

confidence: 99%

Machine learning modeling of power delivery networks with varying decoupling capacitors

Liew

Ahmad

Aziz

et al. 2022

IJ-AI

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<p>This paper presents modeling of power delivery network (PDN) impedance with varying decoupling capacitor placements using machine learning techniques. The use of multilayer perceptron artificial neural networks (ANN) and gaussian process regression (GPR) techniques are explored, and the effects of the hyperparameters such as the number of hidden neurons in the ANN, and the choice of kernel functions in the GPR are investigated. The best performing networks in each case are selected and compared in terms of accuracy using test data consisting of PDN impedance responses that were never encountered during training. Results show that the GPR models were significantly more accurate than the ANN models, with an average mean absolute error of 5.23 mΩ compared to 11.33 mΩ for the ANN.</p>

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Circuit Optimization for 2D and 3D ICs with Machine Learning

Agnesina

Lim

2022

Machine Learning Applications in Electronic Design Automation

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Fast Algorithm for Minimizing the Number of decap in Power Distribution Networks

Cited by 26 publications

References 7 publications

A Novel Physics-Assisted Genetic Algorithm for Decoupling Capacitor Optimization

A Novel Physics-Assisted Genetic Algorithm for Decoupling Capacitor Optimization

Machine learning modeling of power delivery networks with varying decoupling capacitors

Circuit Optimization for 2D and 3D ICs with Machine Learning

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