1. [Publications](/index.php/publications) 2. Graph Learning-Based Arithmetic Block Identification # Graph Learning-Based Arithmetic Block Identification ![Publication image](/sites/default/files/styles/wide/public/default_images/default.jpeg?itok=qUFsuJCP "Publication image") Arithmetic block identification in gate-level netlist is an essential procedure for malicious logic detection, functional verification, or macro-block optimization. We argue that existing methods suffer either scalability or performance issues. To address the problem, we propose a graph learning-based solution that promises to extract desired logic components from a complete design netlist. We further design a novel asynchronous bidirectional graph neural network (ABGNN) dedicated to representation learning on directed acyclic graphs. 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. ## Authors Zhuolun He (CUHK) Ziyi Wang (CUHK) Chen Bai (CUHK) [Haoyu Yang](/index.php/person/haoyu-yang) Bei Yu (CUHK) ## Publication Date Monday, November 1, 2021 ## Published in [ IEEE/ACM International Conference on Computer-Aided Design ](https://iccad.com/) ## Research Area [Algorithms and Numerical Methods](/index.php/research-area/algorithms) [Artificial Intelligence and Machine Learning ](/index.php/research-area/machine-learning-artificial-intelligence) [Circuits and VLSI Design](/index.php/research-area/circuits) ## External Links [Graph Learning-Based Arithmetic Block Identification](https://ieeexplore.ieee.org/document/9643581) ## Uploaded Files [Graph\_Learning-Based\_Arithmetic\_Block\_Identification.pdf](https://d1qx31qr3h6wln.cloudfront.net/publications/Graph_Learning-Based_Arithmetic_Block_Identification.pdf "Open file in new window")816.79 KB ## Copyright This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to .