Late Breaking Results: Encoder-Decoder Generative Diffusion Transformer Towards Push-Button Analog IC Sizing
Azevedo, F. A.
; Lourenço, N.
;
Martins, R. M.
Late Breaking Results: Encoder-Decoder Generative Diffusion Transformer Towards Push-Button Analog IC Sizing, Proc ACM/IEEE Design Automation Conference (DAC), San Francisco, United States, Vol. , pp. - , June, 2025.
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Abstract
In 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.
