DAGSizer: A Directed Graph Convolutional Network Approach to Discrete Gate Sizing of VLSI Graphs

The objective of a leakage recovery step is to make use of positive slack and reduce power by performing appropriate standard-cell swaps such as threshold-voltage ( V th ) or channel-length reassignments. The resulting engineering change order netlist needs to be timing clean. Because this recovery step is performed several times in a physical design flow and involves long runtimes and high tool-license usage, previous works have proposed graph neural network–based frameworks that restrict feature aggregation to three-hop neighborhoods and do not fully consider the directed nature of netlist graphs. As a result, the intermediate node embeddings do not capture the complete structure of the timing graph. In this article, we propose DAGSizer , a framework that exploits the directed acyclic nature of timing graphs to predict cell reassignments in the discrete gate sizing task. Our DAGSizer (Sizer for DAGs) framework is based on a node ordering-aware recurrent message-passing scheme for generating the latent node embeddings. The generated node embeddings absorb the complete information from the fanin cone (predecessors) of the node. To capture the fanout information into the node embeddings, we enable a bidirectional message-passing mechanism. The concatenated latent node embeddings from the forward and reverse graphs are then translated to nodewise delta-delay predictions using a teacher sampling mechanism. With eight possible cell-assignments, the experimental results demonstrate that our model can accurately estimate design-level leakage recovery with an absolute relative error ε model under 5.4%. As compared to our previous work, GRA-LPO, we also demonstrate a significant improvement in the model mean squared error.