About Me
Hi there! My name is Yiqiu (Susan) Sun. I'm a final-year Computer Science PhD student at University of Illinois Urbana-Champaign, expecting to graduate in May 2027. I am fortunate to be advised by Prof. Saugata Ghose. My dissertation tackles the memory wall by making in-memory computing hardware, which performs computation directly inside memory arrays, more programmable, efficient and accurate.
Throughout my PhD, I have found my passion for the following topics:
Memory Systems Performance Engineering Distributed Algorithms (workload balancing) Parallel Programming Models (and languages) Data Formats (precision and arithmetic)
Previously, I received B.S.E. in Computer Engineering from University of Michigan, where I worked with Prof. John P. Hayes and Prof. Trevor Mudge. I also received B.E. in Electrical and Computer Engineering from Shanghai Jiao Tong University (UM-SJTU Joint Institute).
I am on the industry job market for roles starting in summer 2027. Feel free to reach out!
Office: Room 224, CSL Building, 1308 W Main Street MC 228, Urbana, IL, 61801
Email: yiqiu3@illinois.edu
Work Experience
Standard Kernel Co., Member of Technical Staff, Palo Alto, CA (May 2026 - Jul 2026)
Multi-GPU LLM inference profiling (vLLM, SGLang) · FP4/FP8 CUTLASS MoE GEMM and all-reduce optimization · In-context kernel timing and critical-path tooling
Performance Engineering Distributed Algorithms Data Formats
NVIDIA, GPU Architecture PhD Intern, Santa Clara, CA (May 2025 - Aug 2025)
Interconnect scalability · Traffic profiling and visualization · Cross-team hardware-software co-design
Performance Engineering
Samsung Semiconductor, Storage Systems Research Intern, San Jose, CA (May 2023 - Aug 2023)
CXL-SSD caching · Embedding-table lookup latency for recommender systems · Patent
Memory Systems Performance Engineering
Selected Publications
“MaRIMBA: A Framework to Manage Processing-Using-Memory Architectures at Scale,” 2027 IEEE
International Symposium on High-Performance Computer Architecture (HPCA)
Acceptance rate: 17.9%
Yiqiu Sun, Amol Shah, Tharindu Patabandi and Saugata Ghose
Parallel Programming Distributed Algorithms Memory Systems
“Cross-Stack Design Considerations for Viable Processing-Using-Memory Architectures,” 2027 IEEE
International Symposium on High-Performance Computer Architecture (HPCA)
Acceptance rate: 17.9%
Arjun Tyagi, Minh S. Q. Truong, Ryan Wong, Yiqiu Sun, Dawei Xiong and Saugata Ghose
Memory Systems
“The Memory Processing Unit: A Generalized Interface for End-to-End In-Memory Execution,” 2026 IEEE
International Symposium on High-Performance Computer Architecture (HPCA)
Acceptance rate: 19.8%
Minh S. Q. Truong, Yiqiu Sun, Dawei Xiong, Amol Shah, Alexander Glass, Abraham Farrell, James A. Bain, L. Richard Carley and Saugata Ghose
[link]
Memory Systems Parallel Programming
“Tutorial on Simulation for Processing-Using-Memory Systems,” 2024 International Symposium on Computer Architecture (ISCA), Tutorial
ARCANA Research Group, Sandia National Laboratories
[link]
Memory Systems
“Benefits of Stochastic Computing in Hearing Aid Filterbank Design,” 2021 IEEE
Biomedical Circuits and Systems Conference (BioCAS)
Timothy J. Baker, Yiqiu Sun and John P. Hayes
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Data Formats
Research Projects
Dissertation Work
The in-memory computing architecture my dissertation is based on.
Algorithm: Capacity-Aware Matrix Multiplication Partitioning
Model and partition matrix multiplication on in-memory computing hardware by trading operand replication against limited memory capacity and communication cost.
Arithmetic: Flexible-Precision Operation Support
Co-design flexible-precision floating-point arithmetic with numerical algorithms such as Strassen to explore precision, performance, and accuracy trade-offs.
Other Works
Support End-to-End Execution for Processing-Using-Memory
Co-develop the memory processing unit (MPU), a common interface that lets software run end to end on different in-memory computing designs.
Analyzing the Impact of Processing-in-Memory Devices on Scene Reconstruction in Robotics
Evaluate two different depth fusion algorithms executing on a conventional CPU + memory system and a Hybrid Memory Cube with standard CPU cores.
