Ritchie Zhao - RankMe

35 papers A* 8A 5Misc 1Journal 18Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	LatentMoE: Toward Optimal Accuracy per FLOP and Parameter in Mixture of Experts. CoRR Venmugil Elango, Nidhi Bhatia, Roger Waleffe, Rasoul Shafipour, Tomer Asida, Abhinav Khattar, Nave Assaf, Maximilian Golub, Joey Guman, Tiyasa Mitra, Ritchie Zhao, Ritika Borkar, Ran Zilberstein, Mostofa Patwary, Mohammad Shoeybi, Bita Rouhani
2025	J	`jnl`	Beyond the Buzz: A Pragmatic Take on Inference Disaggregation. CoRR 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
2025	J	`jnl`	EMPIRIC: Exploring Missing Pieces in KV Cache Compression for Reducing Computation, Storage, and Latency in Long-Context LLM Inference. ACM SIGOPS Oper. Syst. Rev. Payman Behnam, Yaosheng Fu, Ritchie Zhao, Po-An Tsai, Zhiding Yu, Alexey Tumanov
2025	J	`jnl`	Efficient MoE Serving in the Memory-Bound Regime: Balance Activated Experts, Not Tokens. CoRR Yanpeng Yu, Haiyue Ma, Krish Agarwal, Nicolai Oswald, Qijing Huang, Hugo Linsenmaier, Chunhui Mei, Ritchie Zhao, Ritika Borkar, Bita Darvish Rouhani, David Nellans, Ronny Krashinsky, Anurag Khandelwal
2025	J	`jnl`	Helix Parallelism: Rethinking Sharding Strategies for Interactive Multi-Million-Token LLM Decoding. CoRR Nidhi Bhatia, Ankit More, Ritika Borkar, Tiyasa Mitra, Ramon Matas, Ritchie Zhao, Maximilian Golub, Dheevatsa Mudigere, Brian Pharris, Bita Darvish Rouhani
2025	J	`jnl`	Post-Training Quantization for 3D Medical Image Segmentation: A Practical Study on Real Inference Engines. CoRR Chongyu Qu, Ritchie Zhao, Ye Yu, Bin Liu, Tianyuan Yao, Junchao Zhu, Bennett A. Landman, Yucheng Tang, Yuankai Huo
2025	—	`conf`	ResMoE: Space-efficient Compression of Mixture of Experts LLMs via Residual Restoration. KDD (1) Mengting Ai, Tianxin Wei, Yifan Chen, Zhichen Zeng, Ritchie Zhao, Girish Varatkar, Bita Darvish Rouhani, Xianfeng Tang, Hanghang Tong, Jingrui He
2025	J	`jnl`	ResMoE: Space-efficient Compression of Mixture of Experts LLMs via Residual Restoration. CoRR Mengting Ai, Tianxin Wei, Yifan Chen, Zhichen Zeng, Ritchie Zhao, Girish Varatkar, Bita Darvish Rouhani, Xianfeng Tang, Hanghang Tong, Jingrui He
2025	A*	`conf`	RocketKV: Accelerating Long-Context LLM Inference via Two-Stage KV Cache Compression. ICML Payman Behnam, Yaosheng Fu, Ritchie Zhao, Po-An Tsai, Zhiding Yu, Alexey Tumanov
2025	J	`jnl`	RocketKV: Accelerating Long-Context LLM Inference via Two-Stage KV Cache Compression. CoRR Payman Behnam, Yaosheng Fu, Ritchie Zhao, Po-An Tsai, Zhiding Yu, Alexey Tumanov
2023	J	`jnl`	Microscaling Data Formats for Deep Learning. CoRR 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
2023	J	`jnl`	Shared Microexponents: A Little Shifting Goes a Long Way. CoRR 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
2023	A*	`conf`	With Shared Microexponents, A Little Shifting Goes a Long Way. ISCA 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
2020	A*	`conf`	Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations. ICLR Yichi Zhang, Ritchie Zhao, Weizhe Hua, Nayun Xu, G. Edward Suh, Zhiru Zhang
2020	J	`jnl`	Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations. CoRR Yichi Zhang, Ritchie Zhao, Weizhe Hua, Nayun Xu, G. Edward Suh, Zhiru Zhang
2020	A*	`conf`	Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point. NeurIPS 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
2019	—	`conf`	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. VLSI Circuits 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
2019	A*	`conf`	Building Efficient Deep Neural Networks With Unitary Group Convolutions. CVPR Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Christopher De Sa, Zhiru Zhang
2019	A*	`conf`	Improving Neural Network Quantization without Retraining using Outlier Channel Splitting. ICML Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Christopher De Sa, Zhiru Zhang
2019	J	`jnl`	Improving Neural Network Quantization without Retraining using Outlier Channel Splitting. CoRR Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Christopher De Sa, Zhiru Zhang
2019	J	`jnl`	Overwrite Quantization: Opportunistic Outlier Handling for Neural Network Accelerators. CoRR Ritchie Zhao, Christopher De Sa, Zhiru Zhang
2018	J	`jnl`	Building Efficient Deep Neural Networks with Unitary Group Convolutions. CoRR Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Christopher De Sa, Zhiru Zhang
2018	A	`conf`	Rosetta: A Realistic High-Level Synthesis Benchmark Suite for Software Programmable FPGAs. FPGA 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
2018	J	`jnl`	Serving DNNs in Real Time at Datacenter Scale with Project Brainwave. IEEE Micro 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
2018	J	`jnl`	The Celerity Open-Source 511-Core RISC-V Tiered Accelerator Fabric: Fast Architectures and Design Methodologies for Fast Chips. IEEE Micro 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
2017	A	`conf`	A Parallel Bandit-Based Approach for Autotuning FPGA Compilation. FPGA Chang Xu, Gai Liu, Ritchie Zhao, Stephen Yang, Guojie Luo, Zhiru Zhang
2017	A	`conf`	Accelerating Binarized Convolutional Neural Networks with Software-Programmable FPGAs. FPGA Ritchie Zhao, Weinan Song, Wentao Zhang, Tianwei Xing, Jeng-Hau Lin, Mani B. Srivastava, Rajesh Gupta, Zhiru Zhang
2017	J	`jnl`	Architecture and Synthesis for Area-Efficient Pipelining of Irregular Loop Nests. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. Gai Liu, Mingxing Tan, Steve Dai, Ritchie Zhao, Zhiru Zhang
2017	—	`conf`	Binarized Convolutional Neural Networks with Separable Filters for Efficient Hardware Acceleration. CVPR Workshops Jeng-Hau Lin, Tianwei Xing, Ritchie Zhao, Zhiru Zhang, Mani B. Srivastava, Zhuowen Tu, Rajesh K. Gupta
2017	J	`jnl`	Binarized Convolutional Neural Networks with Separable Filters for Efficient Hardware Acceleration. CoRR Jeng-Hau Lin, Tianwei Xing, Ritchie Zhao, Zhiru Zhang, Mani B. Srivastava, Zhuowen Tu, Rajesh K. Gupta
2017	A	`conf`	Dynamic Hazard Resolution for Pipelining Irregular Loops in High-Level Synthesis. FPGA Steve Dai, Ritchie Zhao, Gai Liu, Shreesha Srinath, Udit Gupta, Christopher Batten, Zhiru Zhang
2017	Misc	`conf`	Enabling adaptive loop pipelining in high-level synthesis. ACSSC Steve Dai, Gai Liu, Ritchie Zhao, Zhiru Zhang
2016	A*	`conf`	Improving high-level synthesis with decoupled data structure optimization. DAC Ritchie Zhao, Gai Liu, Shreesha Srinath, Christopher Batten, Zhiru Zhang
2015	A*	`conf`	Area-efficient pipelining for FPGA-targeted high-level synthesis. DAC Ritchie Zhao, Mingxing Tan, Steve Dai, Zhiru Zhang
2015	A	`conf`	ElasticFlow: A Complexity-Effective Approach for Pipelining Irregular Loop Nests. ICCAD Mingxing Tan, Gai Liu, Ritchie Zhao, Steve Dai, Zhiru Zhang