Presentation
Late Breaking Results: Encoder-Decoder Generative Diffusion Transformer Towards Push-Button Analog IC Sizing
DescriptionIn this paper, disruptive research using generative diffusion models (DMs) with an attention-based encoder-decoder backbone is conducted to automate the sizing of analog integrated circuits (ICs). Unlike time-consuming optimization-based methods, the encoder-decoder DM is able to sample accurate solutions at push-button speed by solving the inverse sizing problem. Experimental results show that the proposed model outperforms the most recent deep learning-based techniques, presenting higher generalization capabilities to performance targets not seen during training.
Event Type
Late Breaking Results
TimeMonday, June 236:00pm - 7:00pm PDT
LocationLevel 2 Lobby
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