Machine learning assisted framework for on-or-off chip power model establishment

A computing device for training a power model for estimating power consumption of a chip, comprising:

• a memory;
• and
• at least one processor coupled to the memory and configured to:
• collect a plurality of data samples, each data sample representing a signal activity of a plurality of signals of the chip;
• select a subset of signals from the plurality of signals as proxies that are correlated with an actual power consumption of the chip according to a criterion;
• train the power model using signal activities of the plurality of signals as inputs and the actual power consumption as an output;
• and
• fine-tune coefficients of the proxies in the power model to adjust an estimation error between an estimated power consumption output by the power model and the actual power consumption.

A computer-readable medium storing computer executable code for operation of a computing device, comprising code to:

• collect a plurality of data samples, each data sample representing a signal activity of a plurality of signals of a chip;
• select a subset of signals from the plurality of signals as proxies that are correlated with an actual power consumption of the chip according to a criterion;
• train a power model using signal activities of the plurality of signals as inputs and the actual power consumption as an output;
• and
• fine-tune coefficients of the proxies in the power model to adjust an estimation error between an estimated power consumption output by the power model and the actual power consumption.