Enabling Machine Learning for Power Modeling via Artificial Netlist Generation

Precise power estimation is increasingly important in developing integrated circuits for both server and edge applications. Recent research focuses on using data-driven methods, like machine learning, to enable power estimation in various design stages, but it requires diverse and large datasets to ensure adequate training. However, the limited number of freely available circuit designs limits the potential of such an approach. One solution could be artificially generating netlists with realistic power behavior to enable accurate training.In this paper, we enhance an existing artificial netlist generation flow [1] for the development of power estimation tools. By enhancing the technology mapping algorithm to take the switching characteristics of individual gates into account, we exert direct influence on the power characteristics of the generated netlists.