6 citations · 6 across the 4 of their papers we have counts for
6 papers
CRouting: Reducing Expensive Distance Calls in Graph-Based Approximate Nearest Neighbor Search
Zhenxin Li, Shuibing He, Jiahao Guo +3
Approximate nearest neighbor search (ANNS) is a crucial problem in information retrieval and AI applications. Recently, there has been a surge of interest in graph-based ANNS algor…
Frenzy: A Memory-Aware Serverless LLM Training System for Heterogeneous GPU Clusters
Zihan Chang, Sheng Xiao, Shuibing He +3
Existing work only effective on a given number of GPUs, often neglecting the complexities involved in manually determining the specific types and quantities of GPUs needed, which c…
HopGNN: Boosting Distributed GNN Training Efficiency via Feature-Centric Model Migration
Weijian Chen, Shuibing He, Haoyang Qu +1
Distributed training of graph neural networks (GNNs) has become a crucial technique for processing large graphs. Prevalent GNN frameworks are model-centric, necessitating the trans…
An Asynchronous Multi-core Accelerator for SNN inference
Zhuo Chen, De Ma, Xiaofei Jin +5
Spiking Neural Networks (SNNs) are extensively utilized in brain-inspired computing and neuroscience research. To enhance the speed and energy efficiency of SNNs, several many-core…
Optimizing Large Model Training through Overlapped Activation Recomputation
Ping Chen, Wenjie Zhang, Shuibing He +10
Large model training often uses recomputation to alleviate memory pressure and pipelines to exploit the parallelism of data, tensors, and devices. However, existing recomputation a…
A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges
ZhengZhao Feng, Rui Wang, TianXing Wang +3
Dynamic Graph Neural Networks (GNNs) combine temporal information with GNNs to capture structural, temporal, and contextual relationships in dynamic graphs simultaneously, leading…