Efficient Arithmetic Block Identification with Graph Learning and Network-flow
Arithmetic block identification in gate-level netlists plays an essential role for various purposes, including malicious logic detection, functional verification, or macro-block optimization. However, current methods usually suffer from either low performance or poor scalability. To address the issue, we come up with a novel framework based on graph learning and network flow analysis, that extracts desired logic components from a complete circuit netlist. We design a novel asynchronous bidirectional graph neural network (ABGNN) dedicated to representation learning on directed acyclic graphs. In addition, we develop a convex cost network-flow-based datapath extraction approach to match the predicted block inputs with predicted block outputs. Experimental results on open-source RISC-V CPU designs demonstrate that our proposed solution significantly outperforms several state-of-the-art arithmetic block identification flows.