Multi-Objective Digital Design Optimization via Improved Drive Granularity Standard Cells

To tackle the complexity of state-of-the-art electronic systems, silicon foundries continuously shrink technology nodes and design automation (EDA) vendors offer hierarchical flows to decompose systems into smaller blocks. However, such a staged methodology consists various levels abstraction, where margins will be accumulated result in degradation overall quality. This limits full use capabilities both process EDA tools. In this work, study drive granularity standard cells is performed an interpolation method proposed for option expansion within original cell libraries. These aim investigate how industrial synthesis tools deal with strength selection using different sets. addition, fully-automated, multi-objective (MO) digital flow introduced power, performance, area (PPA) optimisation based on refinement. population-based search better handles increased difficulty when larger logic libraries, producing optimised solutions than tool case. The achieved experimental results demonstrate improved enhance quality designs significant improvement trading off PPA by MOEDA flow.