Automated Deep Learning Platform for Accelerated Analog Circuit Design

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Automated Deep Learning Platform for Accelerated Analog Circuit Design
Title:
Automated Deep Learning Platform for Accelerated Analog Circuit Design
Journal Title:
2022 IEEE 35th International System-on-Chip Conference (SOCC)
Publication Date:
10 October 2022
Citation:
Dutta, R., James, A., Raju, S., Jeon, Y.-J., Foo, C. S., & Tshun Chuan Chai, K. (2022). Automated Deep Learning Platform for Accelerated Analog Circuit Design. 2022 IEEE 35th International System-on-Chip Conference (SOCC). https://doi.org/10.1109/socc56010.2022.9908139
Abstract:
We present an analog design framework for circuit sizing selection using neural networks. The proposed automated deep learning (ADL) platform uses neural networks (NN) as a differentiable surrogate model for circuit performance to model the nonlinear and high dimensional relationships between sizing performance and circuit performance in analog circuits. Gradient-based constrained optimization is then used to propose new sizing parameters for the desired design closure, which are then verified using EDA tools. If circuit performance falls short of desired performance, the simulation results from the EDA tools are also used as additional training data to update the neural network model for the next design iteration. The tight coupling between NN and EDA tools in an iterative design loop achieves multi-variate design closure and has the capability to synthesize circuits with a significantly reduced number of circuit simulations.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - AME Programmatic Fund
Grant Reference no. : A20H6b0151

This research / project is supported by the A*STAR - AME Programmatic Fund
Grant Reference no. : A19E8b0102
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2164-1706
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