24 citations · 47 across the 7 of their papers we have counts for
7 papers
Exploring the Performance Improvement of Tensor Processing Engines through Transformation in the Bit-weight Dimension of MACs
Qizhe Wu, Huawen Liang, Yuchen Gui +9
General matrix-matrix multiplication (GEMM) is a cornerstone of AI computations, making tensor processing engines (TPEs) increasingly critical in GPUs and domain-specific architect…
Efficient Message Passing Architecture for GCN Training on HBM-based FPGAs with Orthogonal Topology On-Chip Networks
Qizhe Wu, Letian Zhao, Yuchen Gui +1
Graph Convolutional Networks (GCNs) are state-of-the-art deep learning models for representation learning on graphs. However, the efficient training of GCNs is hampered by constrai…
EN-T: Optimizing Tensor Computing Engines Performance via Encoder-Based Methodology
Qizhe Wu, Yuchen Gui, Zhichen Zeng +3
Tensor computations, with matrix multiplication being the primary operation, serve as the fundamental basis for data analysis, physics, machine learning, and deep learning. As the…
How Close Dark Matter Halos and MOND Are to Each Other: Three-Dimensional Tests Based on Gaia DR2
Yongda Zhu, Hai-Xia Ma, Xiao-Bo Dong +7
Aiming at discriminating different gravitational potential models of the Milky Way, we perform tests based on the kinematic data powered by the Gaia DR2 astrometry, over a large ra…
Hardware Acceleration of Sampling Algorithms in Sample and Aggregate Graph Neural Networks
Yuchen Gui, Boyi Wei, Wei Yuan +1
Sampling is an important process in many GNN structures in order to train larger datasets with a smaller computational complexity. However, compared to other processes in GNN (such…
BaPipe: Exploration of Balanced Pipeline Parallelism for DNN Training
Letian Zhao, Rui Xu, Tianqi Wang +5
The size of deep neural networks (DNNs) grows rapidly as the complexity of the machine learning algorithm increases. To satisfy the requirement of computation and memory of DNN tra…
FPDeep: Scalable Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters
Tong Geng, Tianqi Wang, Ang Li +2
Deep Neural Networks (DNNs) have revolutionized numerous applications, but the demand for ever more performance remains unabated. Scaling DNN computations to larger clusters is gen…