195 citations · 936 across the 38 of their papers we have counts for
5 papers · 1 filter
GraphNet: A Large-Scale Computational Graph Dataset for Tensor Compiler Research
Xinqi Li, Yiqun Liu, Shan Jiang +6
We introduce GraphNet, a dataset of 2.7K real-world deep learning computational graphs with rich metadata, spanning six major task categories across multiple deep learning framewor…
CCQ: Convolutional Code for Extreme Low-bit Quantization in LLMs
Zhaojing Zhou, Xunchao Li, Minghao Li +8
The rapid scaling of Large Language Models (LLMs) elevates inference costs and compounds substantial deployment barriers. While quantization to 8 or 4 bits mitigates this, sub-3-bi…
Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials
Mingguo He, Zhewei Wei, Shikun Feng +4
Heterogeneous Graph Neural Networks (HGNNs) have gained significant popularity in various heterogeneous graph learning tasks. However, most existing HGNNs rely on spatial domain-ba…
TA-MoE: Topology-Aware Large Scale Mixture-of-Expert Training
Chang Chen, Min Li, Zhihua Wu +2
Sparsely gated Mixture-of-Expert (MoE) has demonstrated its effectiveness in scaling up deep neural networks to an extreme scale. Despite that numerous efforts have been made to im…
Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters
Yang Xiang, Zhihua Wu, Weibao Gong +15
The ever-growing model size and scale of compute have attracted increasing interests in training deep learning models over multiple nodes. However, when it comes to training on clo…