38 citations · 92 across the 5 of their papers we have counts for
5 papers
FlexMoE: Scaling Large-scale Sparse Pre-trained Model Training via Dynamic Device Placement
Xiaonan Nie, Xupeng Miao, Zilong Wang +5
With the increasing data volume, there is a trend of using large-scale pre-trained models to store the knowledge into an enormous number of model parameters. The training of these…
PIT: Optimization of Dynamic Sparse Deep Learning Models via Permutation Invariant Transformation
Ningxin Zheng, Huiqiang Jiang, Quanlu Zhang +8
Dynamic sparsity, where the sparsity patterns are unknown until runtime, poses a significant challenge to deep learning. The state-of-the-art sparsity-aware deep learning solutions…
EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate
Xiaonan Nie, Xupeng Miao, Shijie Cao +7
Mixture-of-experts (MoE) is becoming popular due to its success in improving the model quality, especially in Transformers. By routing tokens with a sparse gate to a few experts (i…
Architectural Implications of Graph Neural Networks
Zhihui Zhang, Jingwen Leng, Lingxiao Ma +3
Graph neural networks (GNN) represent an emerging line of deep learning models that operate on graph structures. It is becoming more and more popular due to its high accuracy achie…
Towards Efficient Large-Scale Graph Neural Network Computing
Lingxiao Ma, Zhi Yang, Youshan Miao +4
Recent deep learning models have moved beyond low-dimensional regular grids such as image, video, and speech, to high-dimensional graph-structured data, such as social networks, br…