Transformer Network-Based Reinforcement Learning Method for Power Distribution Network (PDN) Optimization of High Bandwidth Memory (HBM)

Park, HyunWook; Kim, Minsu; Kim, Seongguk; Kim, Keunwoo; Kim, Haeyeon; Shin, Taein; Son, Keeyoung; Sim, Boogyo; Kim, Subin; Jeong, Seungtaek; Hwang, Chulsoon; Kim, Joungho

doi:10.1109/tmtt.2022.3202221

Cited by 23 publications

(4 citation statements)

References 36 publications

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“…Metaheuristic searching algorithms such as genetic algorithm (GA) [4], [5], [6], [7], [8], [9] and particle swarm optimization (PSO) [10] have been proposed for decap optimization and demonstrate good performance in finding solutions with the minimum number of decaps. 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%

“…Our code is open-source and available at https://github.com/SoFoolish250/ Physics-Assisted-Genetic-Algorithm-PAGA-for-Decap-Optimization. [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.…”

Section: Introductionmentioning

confidence: 99%

“…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. Moreover, most of these methods assume that all decap types can be placed at each candidate location; however, in real designs, there are usually different constraints on decap sizes at different decap locations.…”

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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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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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show abstract

Section: Introductionmentioning

confidence: 99%

A Novel Physics-Assisted Genetic Algorithm(PAGA) for Decoupling Capacitor Optimization

Jiang,

Zhang,

Tan

et al. 2023

Preprint

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<p>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 low-priority 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 state-of-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 large-scale problems, which brings decap optimization algorithms to a new benchmark.</p>

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Certain Investigations on Eye Diagram Observation for Different Frequencies in Printed Circuit Board

Shan

Prakash

2023

Lecture Notes on Data Engineering and Communications Technologies

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Transformer Network-Based Reinforcement Learning Method for Power Distribution Network (PDN) Optimization of High Bandwidth Memory (HBM)

Cited by 23 publications

References 36 publications

A Novel Physics-Assisted Genetic Algorithm for Decoupling Capacitor Optimization

A Novel Physics-Assisted Genetic Algorithm for Decoupling Capacitor Optimization

A Novel Physics-Assisted Genetic Algorithm(PAGA) for Decoupling Capacitor Optimization

Certain Investigations on Eye Diagram Observation for Different Frequencies in Printed Circuit Board

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