151 citations · 164 across the 3 of their papers we have counts for
5 papers
Dense Pruning of Pointwise Convolutions in the Frequency Domain
Mark Buckler, Neil Adit, Yuwei Hu +2
Depthwise separable convolutions and frequency-domain convolutions are two recent ideas for building efficient convolutional neural networks. They are seemingly incompatible: the v…
FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems
Yuwei Hu, Zihao Ye, Minjie Wang +6
Graph neural networks (GNNs) are gaining increasing popularity as a promising approach to machine learning on graphs. Unlike traditional graph workloads where each vertex/edge is a…
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
Ritchie Zhao, Yuwei Hu, Jordan Dotzel +2
Quantization can improve the execution latency and energy efficiency of neural networks on both commodity GPUs and specialized accelerators. The majority of existing literature foc…
Building Efficient Deep Neural Networks with Unitary Group Convolutions
Ritchie Zhao, Yuwei Hu, Jordan Dotzel +2
We propose unitary group convolutions (UGConvs), a building block for CNNs which compose a group convolution with unitary transforms in feature space to learn a richer set of repre…
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning
Tianqi Chen, Thierry Moreau, Ziheng Jiang +9
There is an increasing need to bring machine learning to a wide diversity of hardware devices. Current frameworks rely on vendor-specific operator libraries and optimize for a narr…