Neural graph with machine learning trained model for circuit manufacturing
AssigneeNVIDIA Corporation
InventorsRenaud Gelin, Karthik Sundararaman, Kaushik Narayanun
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Abstract
Graph-based mechanisms to model and control the synthesis of circuits on a layer basis, including a deep learning model trained on light-weight graph structures rather than complex circuit designs encoded in collateral files generated by EDA systems, and utilizing graph-based algorithms one the graph structure to determine candidate circuit structures and synthesis processes.
Claims20
A system for synthesizing a circuit, the system comprising:
- an encoder configured to convert outputs of an Electronic Design Automation (EDA) system into a first graph structure encoding physical characteristics of the circuit;
- a first neural network comprising a plurality of filtering layers, the first neural network configured to reduce the first graph structure into a second graph structure;
- and
- a second neural network configured to transform the reduced graph structure into updates to the first graph structure.
A circuit synthesis control process comprising:
- encoding file outputs of an Electronic Design Automation (EDA) system into a first graph structure comprising characteristics of a circuit;
- filtering the first graph structure through a first neural network to generate a second graph structure;
- and
- transforming the second graph structure through a second neural network to generate updates to the first graph structure.
A non-transitory machine-readable medium comprising instructions that, when applied to one or more graphics processing unit of a computer system, configure the computer system to:
- encode collateral files generated by an Electronic Design Automation (EDA) system into a first graph structure comprising characteristics of a circuit;
- filter the first graph structure through a first neural network to generate a second graph structure;
- and
- transform the second graph structure through a second neural network to generate updates to the first graph structure.
Dependencies are inferred from the claim text.