Demystifying Bayesian Inference Workloads. - Yu Emma Wang; Yuhao Zhu 001; Glenn G. Ko; Brandon Reagen; Gu-Yeon Wei; David Brooks 001 (ISPASS, 2019)

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; et al. (CoRR, 2020)

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 002; Ketan Mandke; Rezsa Farahani; Yu Emma Wang; Berkin Ilbeyi; et al. (PACT, 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 (MLSys, 2021)

Exploiting Parallelism Opportunities with Deep Learning Frameworks. - Yu Emma Wang; Carole-Jean Wu; Xiaodong Wang 020; Kim M. Hazelwood; David Brooks 001 (ACM Trans. Archit. Code Optim., 2021)

GLaM: Efficient Scaling of Language Models with Mixture-of-Experts. - Nan Du 002; Yanping Huang; Andrew M. Dai; Simon Tong; Dmitry Lepikhin; Yuanzhong Xu; Maxim Krikun; Yanqi Zhou; Adams Wei Yu; Orhan Firat; Barret Zoph; Liam Fedus; et al. (ICML, 2022)

AutoDistill: an End-to-End Framework to Explore and Distill Hardware-Efficient Language Models. - Xiaofan Zhang 001; Zongwei Zhou; Deming Chen; Yu Emma Wang (CoRR, 2022)

Mixed Precision Post Training Quantization of Neural Networks with Sensitivity Guided Search. - Clemens JS Schaefer; Elfie Guo; Caitlin Stanton; Xiaofan Zhang 001; Tom Jablin; Navid Lambert-Shirzad; Jian Li; Chiachen Chou; Siddharth Joshi 001; Yu Emma Wang (CoRR, 2023)

Augmenting Hessians with Inter-Layer Dependencies for Mixed-Precision Post-Training Quantization. - Clemens JS Schaefer; Navid Lambert-Shirzad; Xiaofan Zhang 001; Chiachen Chou; Tom Jablin; Jian Li; Elfie Guo; Caitlin Stanton; Siddharth Joshi 001; Yu Emma Wang (CoRR, 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 001 (CoRR, 2023)

Hadamard Domain Training with Integers for Class Incremental Quantized Learning. - Martin Schiemer; Clemens JS Schaefer; Mark James Horeni; Yu Emma Wang; Juan Ye; Siddharth Joshi 001 (CoLLAs, 2024)

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput. - Arissa Wongpanich; Tayo Oguntebi; Jose Baiocchi Paredes; Yu Emma Wang; Phitchaya Mangpo Phothilimthana; Ritwika Mitra; Zongwei Zhou; Naveen Kumar; Vijay Janapa Reddi (CoRR, 2025)