This work presents a novel reconfigurable architecture for Low Latency Graph Neural
Network (LL-GNN) designs for particle detectors, delivering unprecedented low latency …

Researchers have resorted to model quantization to compress and accelerate graph neural
networks (GNNs). Nevertheless, several challenges remain:(1) quantization functions …

The irregular adjacency matrix and the mismatched computation patterns of Aggregation
and Combination phases make Graph Neural Networks (GNNs) challenging to compute …

Classic Graph Neural Network (GNN) inference approaches, designed for static graphs, are
ill-suited for streaming graphs that evolve with time. The dynamism intrinsic to streaming …

Abstract Graph-based Artificial Intelligence (GraphAI) encompasses AI problems formulated
using graphs, operating on graphs, or relying on graph structures for learning …

A typical optimization of customized accelerators for error-tolerant applications such as
multimedia, recognition, and classification is to replace traditional arithmetic units like …

Graph neural networks (GNN) are vital for analyzing real-world problems (eg, network
analysis, drug interaction, electronic design automation, e-commerce) that use graph …

Deep learning (DL) systems are revolutionizing technology across various fields. These
breakthroughs are driven by the availability of big data, tremendous growth in computational …

JAIST Repository Page 1 Japan Advanced Institute of Science and Technology JAIST
Repository https://dspace.jaist.ac.jp/ Title [課題研究報告書]グラフニューラルネットワークの実装 …