A GNN Computing-in-Memory Macro and Accelerator with Analog-Digital Hybrid Transformation and CAMenabled Search-reduce

Yipeng Wang, Shanshan Xie, Jacob N. Rohan, Meizhi Wang, Mengtian Yang, Sirish Oruganti, Jaydeep P. Kulkarni

Abstract: Graph Neural Networks (GNN) recently find many exciting applications. Despite previous approaches [1], [2], accelerating spatial GNN remains challenging due to its unbalanced computing flow, poor locality, high sparsity, and high memory bandwidth requirements, especially for edge applications such as real-time motion detectors and point cloud processing. This work presents the first GNN computing-in-memory (CIM) macro and accelerator chip, addressing major issues and achieving up to 78.6 X improvement in system energy efficiency compared with previous implementations.