Items Tagged with 'Machine Learning'

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Data-Efficient Supervised Machine Learning Technique for Practical PCB Noise Decoupling

DesignCon 2023 Best Paper Award Winner

Design of PCB-based PDNs has become a challenge due to rising power consumption, lowering supply voltages, increasing integration density and design complexity. In this paper, we propose an algorithmic procedure using supervised machine learning techniques to provide expert guidance on the PDN design and optimize power supply decoupling capacitors. The proposed method replaces the computationally expensive numerical simulations with faster ANNs.



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