8 citations · 8 across the 4 of their papers we have counts for
4 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…
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…