Patent Document ID: 8468475
Application ID: 13400510

Base Claim:
1. An apparatus for converting a description of a circuit into an abstract model of the circuit, the apparatus comprising: a causality engine coupled to at least one database comprising protocol information relating to the circuit, a model description of the circuit, and simulation data associated with the circuit, the causality engine configured to determine deterministic behavior between input and output signals of the simulation data; and a neural network coupled to the causality engine and configured to generate an abstract model of the circuit approximating a behavior of the circuit based on the determination of deterministic repetitive behavior by the causality engine; wherein, to generate an abstract model of the circuit, the neural network is configured to: generate a system of weighted equations representing the behavior of the circuit; apply input patterns to the system of equations to generate actual output values; calculate an error using a difference between the actual output values and desired values; and modify the weightings in the system of equations based on the calculated error.

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Claim 7:
7. The apparatus of claim 1 , wherein, to generate an abstract model of the circuit, the neural network is further configured to generate fork tables and latency tables, the fork tables comprising state information and path information associated with the circuit and the latency tables comprising a latency period associated with a simulated event.