Presentation
A Data-Driven Automation Method of Liberty Model Characterization for Custom Cells
DescriptionVarious custom cells are used in DRAM and NAND Flash memories to optimize power, performance, and area. Liberty model characterization of the custom cells becomes a time-consuming manual task when an automation tool is unable to extract the timing arc and Spice input decks, called configuration for characterization in this paper, from them. The conventional approach is to enhance the tool's capabilities so that it can accommodate custom cells which were not previously taken into consideration. However, as the majority of cell types are remained unchanged across various projects, the configurations can be reused once manually crafted and verified. This study presented a data-driven approach that automates the Liberty model characterization process by mapping a cell to its corresponding configuration with a neural network. We employed graph neural networks (GNNs) to establish relationships between cell topologies and the configurations. We implemented supervised classifiers based on widely used GNNs such as GCN, GraphSAGE, GAT, and GIN, and compare the classification accuracies and the numbers of parameters. With GNNs, our method reached over 94% accuracy, while the traditional rule-based methods using naming convention or ad-hoc connectivity rule scored below 75%.
Event Type
Engineering Track Poster
TimeTuesday, June 255:40pm - 5:41pm PDT
LocationLevel 2 Exhibit Hall
Back-End Design
Embedded Systems
Front-End Design
IP