Ritchie Zhao

According to our database, Ritchie Zhao authored at least 27 papers between 2015 and 2025.

Collaborative distances:

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

2025

EMPIRIC: Exploring Missing Pieces in KV Cache Compression for Reducing Computation, Storage, and Latency in Long-Context LLM Inference.

Payman Behnam, Yaosheng Fu, Ritchie Zhao, Po-An Tsai, Zhiding Yu, Alexey Tumanov

ACM SIGOPS Oper. Syst. Rev., July, 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, July, 2025

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, Bita Darvish Rouhani

CoRR, June, 2025

RocketKV: Accelerating Long-Context LLM Inference via Two-Stage KV Cache Compression.

Payman Behnam, Yaosheng Fu, Ritchie Zhao, Po-An Tsai, Zhiding Yu, Alexey Tumanov

CoRR, February, 2025

Post-Training Quantization for 3D Medical Image Segmentation: A Practical Study on Real Inference Engines.

Chongyu Qu, Ritchie Zhao, Ye Yu, Bin Liu, Tianyuan Yao, Junchao Zhu, Bennett A. Landman, Yucheng Tang, Yuankai Huo

CoRR, January, 2025

ResMoE: Space-efficient Compression of Mixture of Experts LLMs via Residual Restoration.

Mengting Ai, Tianxin Wei, Yifan Chen, Zhichen Zeng, Ritchie Zhao, Girish Varatkar, Bita Darvish Rouhani, Xianfeng Tang, Hanghang Tong, Jingrui He

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, V.1, 2025

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, Maximilian Golub, Alexander Heinecke, Phil James-Roxby, Dharmesh Jani, Gaurav Kolhe, Martin Langhammer, Ada Li, Levi Melnick, Maral Mesmakhosroshahi, Andres Rodriguez, Michael Schulte, Rasoul Shafipour, Lei Shao, Michael Y. Siu, Pradeep Dubey, Paulius Micikevicius, Maxim Naumov, Colin Verilli, Ralph Wittig, Doug Burger, Eric S. Chung

CoRR, 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, Dimitry Melts, Jasmine Klar, Renee L'Heureux, Matt Perry, Doug Burger, Eric S. Chung, Zhaoxia Deng, Sam Naghshineh, Jongsoo Park, Maxim Naumov

CoRR, 2023

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, Dimitry Melts, Jasmine Klar, Renee L'Heureux, Matt Perry, Doug Burger, Eric S. Chung, Zhaoxia (Summer) Deng, Sam Naghshineh, Jongsoo Park, Maxim Naumov

Proceedings of the 50th Annual International Symposium on Computer Architecture, 2023

2020

Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point.

Bita Darvish Rouhani, Daniel Lo, Ritchie Zhao, Ming Liu, Jeremy Fowers, Kalin Ovtcharov, Anna Vinogradsky, Sarah Massengill, Lita Yang, Ray Bittner, Alessandro Forin, Haishan Zhu, Taesik Na, Prerak Patel, Shuai Che, Lok Chand Koppaka, Xia Song, Subhojit Som, Kaustav Das, Saurabh Tiwary, Steven K. Reinhardt, Sitaram Lanka, Eric S. Chung, Doug Burger

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations.

Yichi Zhang, Ritchie Zhao, Weizhe Hua, Nayun Xu, G. Edward Suh, Zhiru Zhang

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Overwrite Quantization: Opportunistic Outlier Handling for Neural Network Accelerators.

Ritchie Zhao, Christopher De Sa, Zhiru Zhang

CoRR, 2019

A 1.4 GHz 695 Giga Risc-V Inst/s 496-Core Manycore Processor With Mesh On-Chip Network and an All-Digital Synthesized PLL in 16nm CMOS.

Austin Rovinski, Chun Zhao, Khalid Al-Hawaj, Paul Gao, Shaolin Xie, Christopher Torng, Scott Davidson, Aporva Amarnath, Luis Vega, Bandhav Veluri, Anuj Rao, Tutu Ajayi, Julian Puscar, Steve Dai, Ritchie Zhao, Dustin Richmond, Zhiru Zhang, Ian Galton, Christopher Batten, Michael B. Taylor, Ronald G. Dreslinski

Proceedings of the 2019 Symposium on VLSI Circuits, Kyoto, Japan, June 9-14, 2019, 2019

Improving Neural Network Quantization without Retraining using Outlier Channel Splitting.

Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Christopher De Sa, Zhiru Zhang

Proceedings of the 36th International Conference on Machine Learning, 2019

Building Efficient Deep Neural Networks With Unitary Group Convolutions.

Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Christopher De Sa, Zhiru Zhang

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

The Celerity Open-Source 511-Core RISC-V Tiered Accelerator Fabric: Fast Architectures and Design Methodologies for Fast Chips.

Scott Davidson, Shaolin Xie, Christopher Torng, Khalid Al-Hawaj, Austin Rovinski, Tutu Ajayi, Luis Vega, Chun Zhao, Ritchie Zhao, Steve Dai, Aporva Amarnath, Bandhav Veluri, Paul Gao, Anuj Rao, Gai Liu, Rajesh K. Gupta, Zhiru Zhang, Ronald G. Dreslinski, Christopher Batten, Michael B. Taylor

IEEE Micro, 2018

Serving DNNs in Real Time at Datacenter Scale with Project Brainwave.

Eric S. Chung, Jeremy Fowers, Kalin Ovtcharov, Michael Papamichael, Adrian M. Caulfield, Todd Massengill, Ming Liu, Daniel Lo, Shlomi Alkalay, Michael Haselman, Maleen Abeydeera, Logan Adams, Hari Angepat, Christian Boehn, Derek Chiou, Oren Firestein, Alessandro Forin, Kang Su Gatlin, Mahdi Ghandi, Stephen Heil, Kyle Holohan, Ahmad El Husseini, Tamás Juhász, Kara Kagi, Ratna Kovvuri, Sitaram Lanka, Friedel van Megen, Dima Mukhortov, Prerak Patel, Brandon Perez, Amanda Rapsang, Steven K. Reinhardt, Bita Rouhani, Adam Sapek, Raja Seera, Sangeetha Shekar, Balaji Sridharan, Gabriel Weisz, Lisa Woods, Phillip Yi Xiao, Dan Zhang, Ritchie Zhao, Doug Burger

IEEE Micro, 2018

Rosetta: A Realistic High-Level Synthesis Benchmark Suite for Software Programmable FPGAs.

Yuan Zhou, Udit Gupta, Steve Dai, Ritchie Zhao, Nitish Kumar Srivastava, Hanchen Jin, Joseph Featherston, Yi-Hsiang Lai, Gai Liu, Gustavo Angarita Velasquez, Wenping Wang, Zhiru Zhang

Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2018

2017

Architecture and Synthesis for Area-Efficient Pipelining of Irregular Loop Nests.

Gai Liu, Mingxing Tan, Steve Dai, Ritchie Zhao, Zhiru Zhang

IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2017

Accelerating Binarized Convolutional Neural Networks with Software-Programmable FPGAs.

Ritchie Zhao, Weinan Song, Wentao Zhang, Tianwei Xing, Jeng-Hau Lin, Mani B. Srivastava, Rajesh Gupta, Zhiru Zhang

Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2017

A Parallel Bandit-Based Approach for Autotuning FPGA Compilation.

Chang Xu, Gai Liu, Ritchie Zhao, Stephen Yang, Guojie Luo, Zhiru Zhang

Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2017

Dynamic Hazard Resolution for Pipelining Irregular Loops in High-Level Synthesis.

Steve Dai, Ritchie Zhao, Gai Liu, Shreesha Srinath, Udit Gupta, Christopher Batten, Zhiru Zhang

Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2017

Binarized Convolutional Neural Networks with Separable Filters for Efficient Hardware Acceleration.

Jeng-Hau Lin, Tianwei Xing, Ritchie Zhao, Zhiru Zhang, Mani B. Srivastava, Zhuowen Tu, Rajesh K. Gupta

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017

Enabling adaptive loop pipelining in high-level synthesis.

Steve Dai, Gai Liu, Ritchie Zhao, Zhiru Zhang

Proceedings of the 51st Asilomar Conference on Signals, Systems, and Computers, 2017

2016

Improving high-level synthesis with decoupled data structure optimization.

Ritchie Zhao, Gai Liu, Shreesha Srinath, Christopher Batten, Zhiru Zhang

Proceedings of the 53rd Annual Design Automation Conference, 2016

2015

ElasticFlow: A Complexity-Effective Approach for Pipelining Irregular Loop Nests.

Mingxing Tan, Gai Liu, Ritchie Zhao, Steve Dai, Zhiru Zhang

Proceedings of the IEEE/ACM International Conference on Computer-Aided Design, 2015

Area-efficient pipelining for FPGA-targeted high-level synthesis.

Ritchie Zhao, Mingxing Tan, Steve Dai, Zhiru Zhang

Proceedings of the 52nd Annual Design Automation Conference, 2015