82 citations · 316 across the 13 of their papers we have counts for
13 papers
Bring Your Own Codegen to Deep Learning Compiler
Zhi Chen, Cody Hao Yu, Trevor Morris +6
Deep neural networks (DNNs) have been ubiquitously applied in many applications, and accelerators are emerged as an enabler to support the fast and efficient inference tasks of the…
UNIT: Unifying Tensorized Instruction Compilation
Jian Weng, Animesh Jain, Jie Wang +3
Because of the increasing demand for computation in DNN, researchers develope both hardware and software mechanisms to reduce the compute and memory burden. A widely adopted approa…
HAWQV3: Dyadic Neural Network Quantization
Zhewei Yao, Zhen Dong, Zhangcheng Zheng +8
Current low-precision quantization algorithms often have the hidden cost of conversion back and forth from floating point to quantized integer values. This hidden cost limits the l…
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…
Is Network the Bottleneck of Distributed Training?
Zhen Zhang, Chaokun Chang, Haibin Lin +3
Recently there has been a surge of research on improving the communication efficiency of distributed training. However, little work has been done to systematically understand wheth…
Efficient Execution of Quantized Deep Learning Models: A Compiler Approach
Animesh Jain, Shoubhik Bhattacharya, Masahiro Masuda +2
A growing number of applications implement predictive functions using deep learning models, which require heavy use of compute and memory. One popular technique for increasing reso…