RAF: Holistic Compilation for Deep Learning Model Training

1 code implementation • 8 Mar 2023 • Cody Hao Yu, Haozheng Fan, Guangtai Huang, Zhen Jia, Yizhi Liu, Jie Wang, Zach Zheng, Yuan Zhou, Haichen Shen, Junru Shao, Mu Li, Yida Wang

In this paper, we present RAF, a deep learning compiler for training.

Graph Generation

Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference

no code implementations • 4 Jun 2020 • Haichen Shen, Jared Roesch, Zhi Chen, Wei Chen, Yong Wu, Mu Li, Vin Sharma, Zachary Tatlock, Yida Wang

Modern deep neural networks increasingly make use of features such as dynamic control flow, data structures and dynamic tensor shapes.

TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

1 code implementation • 12 Feb 2018 • Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

Experimental results show that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art, hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs.

Fast Video Classification via Adaptive Cascading of Deep Models

no code implementations • CVPR 2017 • Haichen Shen, Seungyeop Han, Matthai Philipose, Arvind Krishnamurthy

Recent advances have enabled "oracle" classifiers that can classify across many classes and input distributions with high accuracy without retraining.

Classification Decision Making +2

Visualizing NLP annotations for Crowdsourcing

no code implementations • 25 Aug 2015 • Hanchuan Li, Haichen Shen, Shengliang Xu, Congle Zhang

User studies show our toolkit is very friendly to NLP non-experts, and allow them to produce high quality labels for several sophisticated problems.

Clustering