Conference proceeding
Transfer of Performance Models Across Analog Circuit Topologies with Graph Neural Networks
MLCAD '22: PROCEEDINGS OF THE 2022 ACM/IEEE 4TH WORKSHOP ON MACHINE LEARNING FOR CAD (MLCAD), pp 159-165
01 Jan 2022
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
In this work, graph neural networks (GNNs) and transfer learning are leveraged to transfer device sizing knowledge learned from data of related analog circuit topologies to predict the performance of a new topology. A graph is generated from the netlist of a circuit, with nodes representing the devices and edges the connections between devices. To allow for the simultaneous training of GNNs on data of multiple topologies, graph isomorphism networks are adopted to address the limitation of graph convolutional networks in distinguishing between different graph structures. The techniques are applied to transfer predictions of performance across four op-amp topologies in a 65 nm technology, with 10000 sets of sizing and performance evaluations sampled for each circuit. Two scenarios, zero-shot learning and few-shot learning, are considered based on the availability of data in the target domain. Results from the analysis indicate that zero-shot learning with GNNs trained on all the data of the three related topologies is effective for coarse estimates of the performance of the fourth unseen circuit without requiring any data from the fourth circuit. Few-shot learning by fine-tuning the GNNs with a small dataset of 100 points from the target topology after pre-training on data from the other three topologies further boosts the model performance. The fine-tuned GNNs outperform the baseline artificial neural networks (ANNs) trained on the same dataset of 100 points from the target topology with an average reduction in the root-mean-square error of 70.6%. Applying the proposed techniques, specifically GNNs and transfer learning, improves the sample efficiency of the performance models of the analog ICs through the transfer of predictions across related circuit topologies.
Metrics
Details
- Title
- Transfer of Performance Models Across Analog Circuit Topologies with Graph Neural Networks
- Creators
- Zhengfeng Wu - Drexel UniversityIoannis Savidis - Drexel UniversityAssoc Comp Machinery
- Publication Details
- MLCAD '22: PROCEEDINGS OF THE 2022 ACM/IEEE 4TH WORKSHOP ON MACHINE LEARNING FOR CAD (MLCAD), pp 159-165
- Conference
- MLCAD '22: 2022 ACM/IEEE 4TH WORKSHOP ON MACHINE LEARNING FOR CAD (MLCAD), 4th
- Publisher
- Assoc Computing Machinery
- Number of pages
- 7
- Grant note
- CNS-1751032 / National Science Foundation; National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000866282100026
- Scopus ID
- 2-s2.0-85139237302
- Other Identifier
- 991019240288504721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic
- Engineering, Manufacturing