Symbolic Reference and Hardware Models in Python: A New Approach to Boosting Hardware Design Efficiency

This article introduces a novel approach to hardware modeling using Python – symbolic models. Traditional hardware design workflows involve multiple models (behavioral, architectural, RTL, etc.) for verification, but debugging can be challenging for complex algorithms and data management. The author proposes using Python symbolic models, tracking data origins instead of the data itself, to simplify the debugging process. Using an image downscaler as an example, the article details the construction and comparison of reference and hardware symbolic models, showcasing the advantages of symbolic models in improving design efficiency and confidence, especially when dealing with complex data management and specification changes.