DropBack: Continuous Pruning During Training. - Maximilian Golub; Guy Lemieux; Mieszko Lis (CoRR, 2018)

Full Deep Neural Network Training On A Pruned Weight Budget. - Maximilian Golub; Guy Lemieux; Mieszko Lis (SysML, 2019)

Procrustes: a Dataflow and Accelerator for Sparse Deep Neural Network Training. - Dingqing Yang; Amin Ghasemazar; Xiaowei Ren; Maximilian Golub; Guy Lemieux; Mieszko Lis (MICRO, 2020)

With Shared Microexponents, A Little Shifting Goes a Long Way. - Bita Darvish Rouhani; Ritchie Zhao; Venmugil Elango; Rasoul Shafipour; Mathew Hall; Maral Mesmakhosroshahi; Ankit More; Levi Melnick; Maximilian Golub; Girish Varatkar; Lai Shao; Gaurav Kolhe; et al. (ISCA, 2023)

Shared Microexponents: A Little Shifting Goes a Long Way. - Bita Rouhani; Ritchie Zhao; Venmugil Elango; Rasoul Shafipour; Mathew Hall; Maral Mesmakhosroshahi; Ankit More; Levi Melnick; Maximilian Golub; Girish Varatkar; Lei Shao; Gaurav Kolhe; et al. (CoRR, 2023)

Microscaling Data Formats for Deep Learning. - Bita Darvish Rouhani; Ritchie Zhao; Ankit More; Mathew Hall; Alireza Khodamoradi; Summer Deng; Dhruv Choudhary; Marius Cornea; Eric Dellinger; Kristof Denolf; Dusan Stosic; Venmugil Elango; et al. (CoRR, 2023)

Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models. - Paramveer S. Dhillon; Somayeh Molaei; Jiaqi Li; Maximilian Golub; Shaochun Zheng; Lionel Peter Robert (CHI, 2024)

Beyond the Buzz: A Pragmatic Take on Inference Disaggregation. - Tiyasa Mitra; Ritika Borkar; Nidhi Bhatia; Ramon Matas; Shivam Raj; Dheevatsa Mudigere; Ritchie Zhao; Maximilian Golub; Arpan Dutta; Sailaja Madduri; Dharmesh Jani; Brian Pharris; et al. (CoRR, 2025)

Helix Parallelism: Rethinking Sharding Strategies for Interactive Multi-Million-Token LLM Decoding. - Nidhi Bhatia; Ankit More; Ritika Borkar; Tiyasa Mitra; Ramon Matas; Ritchie Zhao; Maximilian Golub; Dheevatsa Mudigere; Brian Pharris; Bita Darvish Rouhani (CoRR, 2025)