About Me
This is Zihao Ye, a second-year Ph.D. student at the University of Washington’s Paul G. Allen School of Computer Science and Engineering, advised by Luis Ceze in the SAMPL research group. I also work closely with Tianqi Chen on Apache TVM project.
Prior to joining UW, I spent two years at AWS where I worked with Minjie Wang and Zheng Zhang. I obtained my bachelor’s degree from ACM Honors Class at Shanghai Jiao Tong University.
We are organizing talks at SAMPL, topics include Systems, Architecture, Compilers, Verification and Machine Learning.
Research
I have broad interests in Computer Systems, Compiler, Programming Languages, and Computer Architecture. My current research centers around sparse computation:
- Programming Abstraction/Compilers for Sparse Workloads on Heterogeneous Hardware.
- Domain Specific Accelerator and Memory Architecture for Sparsity.
- Scalable and Affordable Sparse Computation in Data Centers.
Feel free to drop me an email if we have aligned interests, and I’m open to collaborations.
Current Projects
SparseTIR
SparseTIR is a unified abstraction for representing and optimizing sparse/irregular workloads in Deep Learning on top of TVM Tensor IR. It aims to generate efficient code for various sparse formats on heterogeneous hardware.
- [ Documentation ], still under construction.
- [ Paper(preprint) ]
- [ Code ]
Invited Talks
Composable Abstractions for Sparse Compilation in Deep Learning
- Nov 2022 @ Amazon AI
- Oct 2022 @ CRISP Liaison Meeting
- Aug 2022 @ Tsinghua NISF-EFC Group
- Aug 2022 @ Cornell Zhang Research Group
- July 2022 @ Google MLIR Reading Group
- Dec 2021 @ TVMCon
Recent Publications
| Preprint |
SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning.
arXiv preprint arXiv:2207.04606, 2022. |
Activity and Service
- External Review Committee: MLSys 2023
- Artifact Evaluation Committee: MLSys 2022, CGO 2023