Yuze Chi

Graduate Student

VAST lab at UCLA


Yuze Chi (Blaok) is a fourth-year PhD student in Computer Science Department, UCLA. Being advised by Prof. Jason Cong, Yuze has been working on various software / hardware optimization projects in many big data applications, including graph processing, image processing, and genomics. Beyond building accelerators for each of the applications one by one, Yuze is actively working on building unified programming infrastructures for heterogeneous systems so that programmers can benefit from hardware acceleration without having to go through the tedious process of hardware design.


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


  • Ph.D., Compute Science, 2016–now

    University of California, Los Angeles

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

    Tsinghua University


Analysis and Optimization of the Implicit Broadcasts in FPGA HLS to Improve Maximum Frequency. In DAC, 2020.

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FLASH: Fast, ParalleL, and Accurate Simulator for HLS. In TCAD, 2020.


HeteroCL: A Multi-Paradigm Programming Infrastructure for Software-Defined Reconfigurable Computing. In FPGA (Best Paper Award), 2019.

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Rapid Cycle-Accurate Simulator for High-Level Synthesis. In FPGA, 2019.

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SODA: Stencil with Optimized Dataflow Architecture. In ICCAD (Best Paper Candidate), 2018.

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GraphH: A Processing-in-Memory Architecture for Large-scale Graph Processing. In TCAD, 2018.


ForeGraph: Exploring Large-scale Graph Processing on Multi-FPGA Architecture. In FPGA, 2017.

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FPGP: Graph Processing Framework on FPGA A Case Study of Breadth-First Search. In FPGA, 2016.

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NXgraph: An Efficient Graph Processing System on a Single Machine. In ICDE, 2016.

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Test–Retest Reliability of Graph Metrics in High-resolution Functional Connectomics: A Resting-State Functional MRI Study. In CNSNT, 2015.



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