Constrained metric optimization of a system on chip

A system, comprising:

• a memory, storing instructions;
• and
• at least one processor that executes the instructions to:
• receive a first configuration of a device, wherein a configuration comprises stimuli controls and stimuli parameters used as inputs in a device model, and wherein the first configuration is validated against a design constraint;
• determine, with a machine learning engine, a quality of the first configuration based on an estimation of an output parameter, wherein the quality of the first configuration is determined by comparing an output parameter value from the first configuration with a distribution of output parameter values obtained from multiple random samples of configurations, and the output parameter comprises a desired behavior of the device;
• simulate the device in the first configuration of the device when the quality of the first configuration is above a selected threshold;
• request a second configuration of the device when the quality of the first configuration is below the selected threshold;
• obtain a regression based on multiple configurations to determine, for the device, a distribution of output parameter values, wherein each configuration in the regression has a quality above the selected threshold;
• and
• compare the distribution of output parameter values with a baseline of a random regression to adjust the machine learning engine according to a target range of output parameter values.

A non-transitory, computer-readable medium comprising instructions stored in a memory which, when executed by a processor cause a computer to perform a method, the method comprising:

• receiving a first configuration of a device, wherein a configuration comprises stimuli controls and stimuli parameters used as inputs in a device model, and wherein the first configuration is validated against a design constraint;
• determining, with a machine learning engine, a quality of the first configuration based on an estimation of an output parameter, wherein the quality of the first configuration is determined by comparing an output parameter value from the first configuration with a distribution of output parameter values obtained from multiple random samples of configurations, and the output parameter comprises a desired behavior of the device;
• simulating the device in the first configuration when the quality of the first configuration is above a selected threshold;
• requesting a second configuration of the device when the quality of the first configuration is below the selected threshold;
• obtaining a regression based on multiple configurations to determine, for the device, a distribution of output parameter values, wherein each configuration in the regression has a quality above the selected threshold;
• and
• comparing the distribution of output parameter values with a baseline of a random regression to adjust the machine learning engine according to a target range of output parameter values.