Practical Layout-Aware Analog/Mixed-Signal Design Automation with Bayesian Neural Networks

The high simulation cost has been a bottleneck of practical analog/mixed-signal design automation. Many learning-based algorithms require thousands of simulated data points, which is impractical for expensive to simulate circuits. We propose a learning-based algorithm that can be trained using a sma...

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Bibliographic Details
Main Authors: Budak, Ahmet F., Zhu, Keren, Pan, David Z.
Format: Conference Proceeding
Language:English
Subjects:
analog layout automation
analog sizing automation
analog/mixed-signal optimization
Approximation algorithms
Automation
Bayes methods
Design automation
electronic design automation
Layout
Neural networks
Scalability
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Summary:The high simulation cost has been a bottleneck of practical analog/mixed-signal design automation. Many learning-based algorithms require thousands of simulated data points, which is impractical for expensive to simulate circuits. We propose a learning-based algorithm that can be trained using a small amount of data and, therefore, scalable to tasks with expensive simulations. Our efficient algorithm solves the post-layout performance optimization problem where simulations are known to be expensive. Our comprehensive study also solves the schematic-level sizing problem. For efficient optimization, we utilize Bayesian Neural Networks as a regression model to approximate circuit performance. For layout-aware optimization, we handle the problem as a multi-fidelity optimization problem and improve efficiency by exploiting the correlations from cheaper evaluations. We present three test cases to demonstrate the efficiency of our algorithms. Our tests prove that the proposed approach is more efficient than conventional baselines and state-of -the-art algorithms.
ISSN:1558-2434
DOI:10.1109/ICCAD57390.2023.10323923