Yu Emma Wang

According to our database, Yu Emma Wang authored at least 10 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Hadamard Domain Training with Integers for Class Incremental Quantized Learning.

Martin Schiemer, Clemens JS Schaefer, Jayden Parker Vap, Mark James Horeni, Yu Emma Wang, Juan Ye, Siddharth Joshi

CoRR, 2023

Augmenting Hessians with Inter-Layer Dependencies for Mixed-Precision Post-Training Quantization.

Clemens JS Schaefer, Navid Lambert-Shirzad, Xiaofan Zhang, Chiachen Chou, Tom Jablin, Jian Li, Elfie Guo, Caitlin Stanton, Siddharth Joshi, Yu Emma Wang

CoRR, 2023

Mixed Precision Post Training Quantization of Neural Networks with Sensitivity Guided Search.

Clemens JS Schaefer, Elfie Guo, Caitlin Stanton, Xiaofan Zhang, Tom Jablin, Navid Lambert-Shirzad, Jian Li, Chiachen Chou, Siddharth Joshi, Yu Emma Wang

CoRR, 2023

2022

AutoDistill: an End-to-End Framework to Explore and Distill Hardware-Efficient Language Models.

Xiaofan Zhang, Zongwei Zhou, Deming Chen, Yu Emma Wang

CoRR, 2022

GLaM: Efficient Scaling of Language Models with Mixture-of-Experts.

Nan Du, Yanping Huang, Andrew M. Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten P. Bosma, Zongwei Zhou, Tao Wang, Yu Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, Kathleen S. Meier-Hellstern, Toju Duke, Lucas Dixon, Kun Zhang, Quoc V. Le, Yonghui Wu, Zhifeng Chen, Claire Cui

Proceedings of the International Conference on Machine Learning, 2022

2021

Exploiting Parallelism Opportunities with Deep Learning Frameworks.

Yu Emma Wang, Carole-Jean Wu, Xiaodong Wang, Kim M. Hazelwood, David Brooks

ACM Trans. Archit. Code Optim., 2021

Exploring the Limits of Concurrency in ML Training on Google TPUS.

Sameer Kumar, Yu Emma Wang, Cliff Young, James Bradbury, Naveen Kumar, Dehao Chen, Andy Swing

Proceedings of the Fourth Conference on Machine Learning and Systems, 2021

A Flexible Approach to Autotuning Multi-Pass Machine Learning Compilers.

Phitchaya Mangpo Phothilimthana, Amit Sabne, Nikhil Sarda, Karthik Srinivasa Murthy, Yanqi Zhou, Christof Angermueller, Mike Burrows, Sudip Roy, Ketan Mandke, Rezsa Farahani, Yu Emma Wang, Berkin Ilbeyi, Blake A. Hechtman, Bjarke Roune, Shen Wang, Yuanzhong Xu, Samuel J. Kaufman

Proceedings of the 30th International Conference on Parallel Architectures and Compilation Techniques, 2021

2020

Exploring the limits of Concurrency in ML Training on Google TPUs.

Sameer Kumar, James Bradbury, Cliff Young, Yu Emma Wang, Anselm Levskaya, Blake A. Hechtman, Dehao Chen, HyoukJoong Lee, Mehmet Deveci, Naveen Kumar, Pankaj Kanwar, Shibo Wang, Skye Wanderman-Milne, Steve Lacy, Tao Wang, Tayo Oguntebi, Yazhou Zu, Yuanzhong Xu, Andy Swing

CoRR, 2020

2019

Demystifying Bayesian Inference Workloads.

Yu Emma Wang, Yuhao Zhu, Glenn G. Ko, Brandon Reagen, Gu-Yeon Wei, David Brooks

Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software, 2019