Patent ID: 11947503
Assignee: GOOGLE LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 19:
20. The method of claim 18, further comprising training the neural network system with the machine learning training technique to generate a synthetic chip graph drawn from a same distribution as a set of real chip graphs, wherein the training comprises, for one or more real training chip graphs:
processing the real training chip graph using the neural network system;
determining a measure of likelihood for the real training chip graph, wherein determining a measure of likelihood comprises, for each node in the chip graph:
generating a respective edge set for each node in the chip graph, wherein each edge set comprises a tree of one or more leaf vertices, each indicative of one or more routings of inputs to and outputs of the logical operations in the integrated circuit of the real training chip graph;
embedding each edge set to generate one or more edge set embeddings;
processing the one or more edge set embeddings to generate a context embedding for each node by deriving a hierarchy of embeddings from each edge set embedding;
processing the context embedding for each node to determine the likelihood of the training graph, wherein processing the context embedding comprises evaluating the likelihood of a respective edge set of each node in the real training chip graph, in accordance with a set of one or more criteria; and

updating the values of the set of one or more neural network parameters in the neural network system based at least on the likelihood of the real training chip graph.