Area Estimation Framework for Digital Hardware Design using Machine Learning

Digital hardware design usually includes a design space exploration phase in order to determine a viable trade-off between the area and other metrics of the design, such as the performance. As the design space grows exponentially with the number and the range of the design parameters, synthesizing the digital hardware design for a large number of design candidates quickly becomes infeasible. The proposed area estimation framework not only enables a designer to automatically synthesize different design candidates and store the results in a database, but also utilizes machine learning algorithms to approximate the area for future design candidates. Due to its universal approach, the area estimation framework can easily be extended to support arbitrary hardware description languages, synthesis tools, and machine learning algorithms. First experiments using only basic automated hyperparameter tuning already show promising results of above 90% accuracy.