CPC G06F 30/327 (2020.01) [G06F 30/35 (2020.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01)] | 19 Claims |
1. A method for accumulating data used as a training set for training a machine learning model capable of determining one or more characteristics for any IP block, which has a set of input parameters, in order to optimize an IP block, the method comprising:
determining a plurality of vectors of logical parameters for a plurality of IP arrangements;
determining a plurality of vectors of physical parameters for the plurality of IP arrangements;
generating a plurality of composite vectors based on the plurality of vectors of physical parameters and the plurality of vectors of logical parameters;
synthesizing the plurality of composite vectors to generate a gate level netlist for each combination of the plurality of vectors of physical parameters and the plurality of vectors of logical parameters;
generating a timing characterization for each gate level netlist to produce a plurality of timing characterizations; and
updating the training set, in order to provide optimization characteristic for an IP block that is used to optimize the IP block, using data selected from a set including at least one of:
the plurality of vectors of logical parameters;
the plurality of vectors of physical parameters;
the plurality of composite vectors; and
the timing characterization.
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