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Yuze Chi

Biography

Yuze Chi (Blaok) obtained his PhD in Computer Science Department, UCLA. Yuze has been working on various computer system optimization projects in many big data applications, including graph processing, image processing, and genomics. Beyond building optimized solutions for each application one by one, Yuze also worked on unified programming infrastructures for heterogeneous systems. After graduation, Yuze joined Google and started the quest to handle and optimize computing and networking infrastructures at Google scale.

Interests

  • Heterogeneous Systems
  • Hardware Acceleration
  • Big Data Optimization
  • Cloud Computing

Education

  • Ph.D., Compute Science, 2016–2021

    University of California, Los Angeles

  • B.E., Electronic Engineering, 2012–2016

    Tsinghua University

Publications

Moazin Khatti, Xingyu Tian, Yuze Chi, Licheng Guo, Jason Cong, Zhenman Fang. PASTA: Programming and Automation Support for Scalable Task-Parallel HLS Programs on Modern Multi-Die FPGAs. In FCCM, 2023.

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Licheng Guo, Pongstorn Maidee, Yun Zhou, Chris Lavin, Eddie Hung, Wuxi Li, Jason Lau, Weikang Qiao, Yuze Chi, Linghao Song, Yuanlong Xiao, Alireza Kaviani, Zhiru Zhang, Jason Cong. RapidStream 2.0: Automated Parallel Implementation of Latency Insensitive FPGA Designs Through Partial Reconfiguration. In TRETS, 2023.

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Xingyu Tian, Zhifan Ye, Alec Lu, Licheng Guo, Yuze Chi, Zhenman Fang. SASA: A Scalable and Automatic Stencil Acceleration Framework for Optimized Hybrid Spatial and Temporal Parallelism on HBM-based FPGAs. In TRETS, 2023.

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Linghao Song, Licheng Guo, Suhail Basalama, Yuze Chi, Robert F. Lucas, Jason Cong. Callipepla: Stream Centric Instruction Set and Mixed Precision for Accelerating Conjugate Gradient Solver. In FPGA, 2023.

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Yuze Chi, Weikang Qiao, Atefeh Sohrabizadeh, Jie Wang, Jason Cong. Democratizing Domain-Specific Computing. In CACM, 2022.

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Young-kyu Choi, Yuze Chi, Jason Lau, Jason Cong. TARO: Automatic Optimization for Free-Running Kernels in FPGA High-Level Synthesis. In TCAD, 2022.

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Linghao Song, Yuze Chi, Licheng Guo, Jason Cong. Serpens: A High Bandwidth Memory Based Accelerator for General-Purpose Sparse Matrix-Vector Multiplication. In DAC, 2022.

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Linghao Song, Yuze Chi, Jason Cong. PYXIS: An Open-Source Performance Dataset Of Sparse Accelerators. In ICASSP, 2022.

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Atefeh Sohrabizadeh, Yuze Chi, Jason Cong. StreamGCN: Accelerating Graph Convolutional Networks with Streaming Processing. In CICC, 2022.

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Yuze Chi, Licheng Guo, Jason Cong. Accelerating SSSP for Power-Law Graphs. In FPGA, 2022.

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Licheng Guo, Pongstorn Maidee, Yun Zhou, Chris Lavin, Jie Wang, Yuze Chi, Weikang Qiao, Alireza Kaviani, Zhiru Zhang, Jason Cong. RapidStream: Parallel Physical Implementation of FPGA HLS Designs. In FPGA, 2022.

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Linghao Song, Yuze Chi, Atefeh Sohrabizadeh, Young-kyu Choi, Jason Lau, Jason Cong. Sextans: A Streaming Accelerator for General-Purpose Sparse-Matrix Dense-Matrix Multiplication. In FPGA, 2022.

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Yuze Chi. Design Automation and Optimization for Memory-Bound Application Accelerators. 2021.

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Karl Marrett, Muye Zhu, Yuze Chi, Chris Choi, Zhe Chen, Hong-Wei Dong, Chang Sin Park, X. William Yang, Jason Cong. Recut: a Concurrent Framework for Sparse Reconstruction of Neuronal Morphology. In bioRxiv, 2021.

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Atefeh Sohrabizadeh, Yuze Chi, Jason Cong. SPA-GCN: Efficient and Flexible GCN Accelerator with an Application for Graph Similarity Computation. In arXiv, 2021.

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Yuze Chi, Licheng Guo, Jason Lau, Young-kyu Choi, Jie Wang, Jason Cong. Extending High-Level Synthesis for Task-Parallel Programs. In FCCM, 2021.

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Young-kyu Choi, Yuze Chi, Weikang Qiao, Nikola Samardzic, Jason Cong. HBM Connect: High-Performance HLS Interconnect for FPGA HBM. In FPGA, 2021.

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Licheng Guo, Yuze Chi, Jie Wang, Jason Lau, Weikang Qiao, Ecenur Ustun, Zhiru Zhang, Jason Cong. AutoBridge: Coupling Coarse-Grained Floorplanning and Pipelining for High-Frequency HLS Design on Multi-Die FPGAs. In FPGA (Best Paper Award), 2021.

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Young-kyu Choi, Yuze Chi, Jie Wang, Licheng Guo, Jason Cong. When HLS Meets FPGA HBM: Benchmarking and Bandwidth Optimization. In arXiv, 2020.

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Yuze Chi, Jason Cong. Exploiting Computation Reuse for Stencil Accelerators. In DAC, 2020.

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Licheng Guo, Jason Lau, Yuze Chi, Jie Wang, Cody Hao Yu, Zhe Chen, Zhiru Zhang, Jason Cong. Analysis and Optimization of the Implicit Broadcasts in FPGA HLS to Improve Maximum Frequency. In DAC, 2020.

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Jiajie Li, Yuze Chi, Jason Cong. HeteroHalide: From Image Processing DSL to Efficient FPGA Acceleration. In FPGA, 2020.

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Young-kyu Choi, Yuze Chi, Jie Wang, Jason Cong. FLASH: Fast, ParalleL, and Accurate Simulator for HLS. In TCAD, 2020.

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Yi-Hsiang Lai, Yuze Chi, Yuwei Hu, Jie Wang, Cody Hao Yu, Yuan Zhou, Jason Cong, Zhiru Zhang. HeteroCL: A Multi-Paradigm Programming Infrastructure for Software-Defined Reconfigurable Computing. In FPGA (Best Paper Award), 2019.

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Yuze Chi, Young-kyu Choi, Jason Cong, Jie Wang. Rapid Cycle-Accurate Simulator for High-Level Synthesis. In FPGA, 2019.

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Yuze Chi, Jason Cong, Peng Wei, Peipei Zhou. SODA: Stencil with Optimized Dataflow Architecture. In ICCAD (Best Paper Candidate), 2018.

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Guohao Dai, Tianhao Huang, Yuze Chi, Jishen Zhao, Guangyu Sun, Yongpan Liu, Yu Wang, Yuan Xie, Huazhong Yang. GraphH: A Processing-in-Memory Architecture for Large-scale Graph Processing. In TCAD, 2018.

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Yuze Chi, Peipei Zhou, Jason Cong. An Optimal Microarchitecture for Stencil Computation with Data Reuse and Fine-Grained Parallelism. In FPGA, 2018.

Guohao Dai, Tianhao Huang, Yuze Chi, Ningyi Xu, Yu Wang, Huazhong Yang. ForeGraph: Exploring Large-scale Graph Processing on Multi-FPGA Architecture. In FPGA, 2017.

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Guohao Dai, Yuze Chi, Yu Wang, Huazhong Yang. FPGP: Graph Processing Framework on FPGA A Case Study of Breadth-First Search. In FPGA, 2016.

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Yuze Chi, Guohao Dai, Yu Wang, Guangyu Sun, Guoliang Li, Huazhong Yang. NXgraph: An Efficient Graph Processing System on a Single Machine. In ICDE, 2016.

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Hai‐Xiao Du, Xu‐Hong Liao, Qi‐Xiang Lin, Gu‐Shu Li, Yu‐Ze Chi, Xiang Liu, Hua‐Zhong Yang, Yu Wang, Ming‐Rui Xia. Test–Retest Reliability of Graph Metrics in High-resolution Functional Connectomics: A Resting-State Functional MRI Study. In CNSNT, 2015.

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Teaching

I am a teaching assistant for the following courses at UCLA:

  • CS180: Introduction to Algorithms and Complexity, Fall 2017
  • CS259: Customized Computing for Big-Data Applications, Fall 2018
  • CS133: Parallel and Distributed Computing, Winter 2019