GCN-RL circuit designer: Transferable transistor sizing with graph neural networks and reinforcement learning
Name
2005.00406.pdf
Description
Submitted version
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2.54 MB
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Unknown
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4469aa02257b5d63ba392c438eec63be
Author(s)
Wang, Haoxuan
Wang, Kuan
Yang, J
Shen, L
Sun, N
Lee, Hae-Seung
Han, Song
Date Issued
2020
Journal
Proceedings - Design Automation Conference
Publisher
IEEE
Version
Original manuscript
Abstract
© 2020 IEEE. Automatic transistor sizing is a challenging problem in circuit design due to the large design space, complex performance tradeoffs, and fast technology advancements. Although there have been plenty of work on transistor sizing targeting on one circuit, limited research has been done on transferring the knowledge from one circuit to another to reduce the re-design overhead. In this paper, we present GCN-RL Circuit Designer, leveraging reinforcement learning (RL) to transfer the knowledge between different technology nodes and topologies. Moreover, inspired by the simple fact that circuit is a graph, we learn on the circuit topology representation with graph convolutional neural networks (GCN). The GCN-RL agent extracts features of the topology graph whose vertices are transistors, edges are wires. Our learning-based optimization consistently achieves the highest Figures of Merit (FoM) on four different circuits compared with conventional black box optimization methods (Bayesian Optimization, Evolutionary Algorithms), random search and human expert designs. Experiments on transfer learning between five technology nodes and two circuit topologies demonstrate that RL with transfer learning can achieve much higher FoMs than methods without knowledge transfer. Our transferable optimization method makes transistor sizing and design porting more effective and efficient.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/DAC18072.2020.9218757
