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G-MAP: A Graph-Neural Network Based Framework for Memory Access Prediction

Published in IEEE HPEC, 2023

In this paper, we introduce G-MAP, a novel Graph Neural Network-based framework for Memory Access Prediction. First, we propose Mem2Graph, a novel approach mapping a memory access sequence to a graph representation, capturing both the spatial and temporal locality in the memory access sequence which most existing methods fail to do. Second, we implement various GNNs for G-MAP, including Graph Convolutional Network (GCN), Gated Graph Sequence Neural Network (GG-NN), and Graph Attention Network (GAT).

Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://academicpages.github.io/files/paper1.pdf