Learning of Multi-Dimensional Analog Circuits Through Generative Adversarial Network (GAN)

Learning of Multi-Dimensional Analog Circuits Through Generative Adversarial Network (GAN)
Title:
Learning of Multi-Dimensional Analog Circuits Through Generative Adversarial Network (GAN)
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2019 32nd IEEE International System-on-Chip Conference (SOCC)
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Publication Date:
03 September 2019
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Abstract:
Analog circuits are strictly designed under operational, functional and technology constraints. Together, these bounds create a sparse multi-dimensional design optimization space with the scarcity of labeled analog training data making supervised learning methods ineffective. Accurate approximation of multi-target analog circuits, therefore, requires generation of labeled data around dominant bias and with relevant variance. With such an approach, we explore state-of-the-art semi-supervised, generative adversarial network (GAN) towards analog performance modeling. We report on various multi-target analog circuit classification experiments and demonstrate stable GAN performance achieving 2-5% higher accuracy and utilizing only 10% fully simulated manually annotated labeled data against supervised learning methods.
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(c) 2019 IEEE.
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