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ORCIDRitchie Zhao
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2020 – today
- 2023
[c15]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:
With Shared Microexponents, A Little Shifting Goes a Long Way. ISCA 2023: 83:1-83:13- [i7]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:
Shared Microexponents: A Little Shifting Goes a Long Way. CoRR abs/2302.08007 (2023) - [i6]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:
Microscaling Data Formats for Deep Learning. CoRR abs/2310.10537 (2023) - 2020
- [c14]Yichi Zhang, Ritchie Zhao, Weizhe Hua, Nayun Xu, G. Edward Suh, Zhiru Zhang:
Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations. ICLR 2020 - [c13]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:
Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point. NeurIPS 2020 - [i5]Yichi Zhang, Ritchie Zhao, Weizhe Hua, Nayun Xu, G. Edward Suh, Zhiru Zhang:
Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations. CoRR abs/2002.07136 (2020)
2010 – 2019
- 2019
- [c12]Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Christopher De Sa, Zhiru Zhang:
Building Efficient Deep Neural Networks With Unitary Group Convolutions. CVPR 2019: 11303-11312 - [c11]Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Christopher De Sa, Zhiru Zhang:
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting. ICML 2019: 7543-7552 - [c10]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:
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 2019: 30- - [i4]Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Christopher De Sa, Zhiru Zhang:
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting. CoRR abs/1901.09504 (2019) - [i3]Ritchie Zhao, Christopher De Sa, Zhiru Zhang:
Overwrite Quantization: Opportunistic Outlier Handling for Neural Network Accelerators. CoRR abs/1910.06909 (2019) - 2018
- [j3]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:
Serving DNNs in Real Time at Datacenter Scale with Project Brainwave. IEEE Micro 38(2): 8-20 (2018) - [j2]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:
The Celerity Open-Source 511-Core RISC-V Tiered Accelerator Fabric: Fast Architectures and Design Methodologies for Fast Chips. IEEE Micro 38(2): 30-41 (2018) - [c9]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:
Rosetta: A Realistic High-Level Synthesis Benchmark Suite for Software Programmable FPGAs. FPGA 2018: 269-278 - [i2]Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Christopher De Sa, Zhiru Zhang:
Building Efficient Deep Neural Networks with Unitary Group Convolutions. CoRR abs/1811.07755 (2018) - 2017
- [j1]Gai Liu, Mingxing Tan, Steve Dai, Ritchie Zhao, Zhiru Zhang:
Architecture and Synthesis for Area-Efficient Pipelining of Irregular Loop Nests. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 36(11): 1817-1830 (2017) - [c8]Steve Dai, Gai Liu, Ritchie Zhao, Zhiru Zhang:
Enabling adaptive loop pipelining in high-level synthesis. ACSSC 2017: 131-135 - [c7]Jeng-Hau Lin, Tianwei Xing, Ritchie Zhao, Zhiru Zhang, Mani B. Srivastava, Zhuowen Tu, Rajesh K. Gupta:
Binarized Convolutional Neural Networks with Separable Filters for Efficient Hardware Acceleration. CVPR Workshops 2017: 344-352 - [c6]Ritchie Zhao, Weinan Song, Wentao Zhang, Tianwei Xing, Jeng-Hau Lin, Mani B. Srivastava, Rajesh Gupta, Zhiru Zhang:
Accelerating Binarized Convolutional Neural Networks with Software-Programmable FPGAs. FPGA 2017: 15-24 - [c5]Chang Xu, Gai Liu, Ritchie Zhao, Stephen Yang, Guojie Luo, Zhiru Zhang:
A Parallel Bandit-Based Approach for Autotuning FPGA Compilation. FPGA 2017: 157-166 - [c4]Steve Dai, Ritchie Zhao, Gai Liu, Shreesha Srinath, Udit Gupta, Christopher Batten, Zhiru Zhang:
Dynamic Hazard Resolution for Pipelining Irregular Loops in High-Level Synthesis. FPGA 2017: 189-194 - [i1]Jeng-Hau Lin, Tianwei Xing, Ritchie Zhao, Zhiru Zhang, Mani B. Srivastava, Zhuowen Tu, Rajesh K. Gupta:
Binarized Convolutional Neural Networks with Separable Filters for Efficient Hardware Acceleration. CoRR abs/1707.04693 (2017) - 2016
- [c3]Ritchie Zhao, Gai Liu, Shreesha Srinath, Christopher Batten, Zhiru Zhang:
Improving high-level synthesis with decoupled data structure optimization. DAC 2016: 137:1-137:6 - 2015
- [c2]Ritchie Zhao, Mingxing Tan, Steve Dai, Zhiru Zhang:
Area-efficient pipelining for FPGA-targeted high-level synthesis. DAC 2015: 157:1-157:6 - [c1]Mingxing Tan, Gai Liu, Ritchie Zhao, Steve Dai, Zhiru Zhang:
ElasticFlow: A Complexity-Effective Approach for Pipelining Irregular Loop Nests. ICCAD 2015: 78-85
Coauthor Index
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