Zitao Mo

most cited

A^{2} Q

: Aggregation-Aware Quantization for Graph Neural Networks

1 citations · 1 across the 3 of their papers we have counts for

collaborators

3 papers

cs.AR2025

GCC: A 3DGS Inference Architecture with Gaussian-Wise and Cross-Stage Conditional Processing

Minnan Pei, Gang Li, Junwen Si +6

3D Gaussian Splatting (3DGS) has emerged as a leading neural rendering technique for high-fidelity view synthesis, prompting the development of dedicated 3DGS accelerators for reso…

cs.AR2023

MEGA: A Memory-Efficient GNN Accelerator Exploiting Degree-Aware Mixed-Precision Quantization

Zeyu Zhu, Fanrong Li, Gang Li +5

Graph Neural Networks (GNNs) are becoming a promising technique in various domains due to their excellent capabilities in modeling non-Euclidean data. Although a spectrum of accele…

cs.LG2023★ 1 cited

$A^{2} Q$ : Aggregation-Aware Quantization for Graph Neural Networks

Zeyu Zhu, Fanrong Li, Zitao Mo +5

As graph data size increases, the vast latency and memory consumption during inference pose a significant challenge to the real-world deployment of Graph Neural Networks (GNNs). Wh…

GCC: A 3DGS Inference Architecture with Gaussian-Wise and Cross-Stage Conditional Processing

MEGA: A Memory-Efficient GNN Accelerator Exploiting Degree-Aware Mixed-Precision Quantization

A2Q: Aggregation-Aware Quantization for Graph Neural Networks

$A^{2} Q$ : Aggregation-Aware Quantization for Graph Neural Networks