Chang Eun (Paul), Song
CV | Linkedin | Github | Google Scholar | Contact
CV | Linkedin | Github | Google Scholar | Contact
My research interests include HW-SW Co-Design of AI Processors, ML Accelerators design for large language models, In-memory computing, and Neuromorphic computing. I work on the end-to-end flow for AI architecture & processor design, from ML optimization to Chip fabrication.
I'm a third-year Ph.D. Candidate in Computer Science and Engineering at the University of California, San Diego (UCSD), co-advised by Prof. Mingu Kang and Prof. Tajana Rosing.
I received a Bachelor's in Electrical Engineering from Korea University in February 2022.
<Skillset>
Circuit Design Tools : Model Sim, Design Vision, iverilog, Cadence tool (Virtuoso, Genus, Innovus, Xcelium, simvision, Pspice), Synopsys tool (Design Complier, VCS, DVE)
Software Languages : Verilog, System Verilog, Python, Pytorch, C, C++, MATLAB
[Latest Updates!] (Click)
[2025-06] Passed the Thesis Proposal (PhD Qualification Exam)!
[2025-06] I won the Best Doctoral Research Award in UCSD CSE!
[2025-05] One paper has been accepted by HOTCHIPS25! (To appear)
[2025-03] One paper has been accepted by VLSI 2025! (To appear)
[2025-03] One paper has been accepted by ISCA 2025! (To appear)
[2024-06] I joined TSMC Technology, Inc. (San Jose, CA) this summer as an AI Hardware Research Intern!
[2024-05] One paper has been accepted by ESSERC 2024! [Link]
[2024-05] One paper has been accepted by ISLPED 2024! [Link]
[2024-02] Passed the Research Exam for PhD degree!
[2023-10] Nominated as Samsung Electronics Global Talent Ph.D. Fellowship by Samsung Electronics Memory Division Solution!
[2023-07] My ReRAM In-memory computing Processor chip has been fabricated using TSMC 40nm!
[2023-04] My first AI Processor chip for XGBoost acceleration has been fabricated using TSMC 65nm!
[2023-01] Joined SEELAB as a PhD Graduate Student Researcher (GSR) co-advised by Prof. Tajana Rosing!
[2022-09] Joined VVIP LAB as a PhD Graduate Student Researcher (GSR) co-advised by Prof. Mingu Kang!
[2022-09] Started PhD degree at University of California, San Diego (UCSD), Computer Science & Engineering!
<Education>
University of California, San Diego (GPA of 3.93/4.0)
2022.09 - Present
Ph.D. Candidate in Computer Science and Engineering (Co-Advisor: Mingu Kang, Tajana Rosing)
Korea University (GPA of 4.17/4.5, Major GPA of 4.43/4.5)
2016.03 - 2022.02
Bachelor of Engineering in Electrical Engineering (Two years absence for military service (2018-2019))
<Publications>
Conference (* Equal Contribution)
[C.6] Clo-HDnn: Continual On-Device Learning Accelerator with Hyperdimensional Computing via Progressive Search
C. E. Song, W. Xu, K. Fan, S. Jain, G. Hota, H. Yang, L. Liu, M. F. Chang, C. H. Diaz, G. Cauwenberghs, T. Rosing, and M. Kang
IEEE Hot Chips Symposium (HCS), 2025 (To appear)
[C.5] Clo-HDnn: A 4.66 TFLOPS/W and 3.78 TOPS/W Continual On-Device Learning Accelerator with Energy-efficient Hyperdimensional Computing via Progressive Search
C. E. Song*, W. Xu*, K. Fan, S. Jain, G. Hota, H. Yang, L. Liu, K. Akarvardar, M. F. Chang, C. H. Diaz, G. Cauwenberghs, T. Rosing, and M. Kang
IEEE Symposium on VLSI Technology and Circuits (VLSI), 2025 (To appear)
[C.4] Hybrid SLC-MLC RRAM Mixed-Signal Processing-in-Memory Architecture for Transformer Acceleration via Gradient Redistribution
C. E Song, P. Bhatnagar, Z. Xia, N. S. Kim, T. Rosing, and M. Kang
ACM/IEEE International Symposium on Computer Architecture (ISCA), 2025 (To appear)
[C.3] FSL-HDnn: A 5.7 TOPS/W End-to-end Few-shot Learning Classifier Accelerator with Feature Extraction and Hyperdimensional Computing,
H. Yang*, C. E. Song*, W. Xu, B. Khaleghi, U. Mallappa, M. Shah, K. Fan, M. Kang, and T. Rosing
IEEE European Solid-State Electronics Research Conference (ESSERC), 2024 [Link]
[C.2] Efficient Transformer Acceleration via Reconfiguration for Encoder and Decoder Models and Sparsity-Aware Algorithm Mapping
C. E. Song*, A. Moradifirouzabadi*, T. Rosing, and M. Kang
ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED), 2024 [Link]
[C.1] 52.5 TOPS/W 1.7GHz Reconfigurable XGBoost Inference Accelerator based on Modular-Unit-Tree with Dynamic Data and Compute Gating
C. E. Song, Y. Li, A. Ramnani, P. Agrawal, P. Agrawal, S. Jang, S. Lee, T. Rosing, and M. Kang
IEEE Custom Integrated Circuits Conference (CICC), 2024 [Link]
Journal (* Equal Contribution)
[J.2] FSL-HDnn: A 40nm Multi-purpose On-Device Learning Accelerator with Integrated Feature Extraction and Hyperdimensional Computing Enabling Fast Edge AI Applications (under review)
W. Xu*, C. E. Song*, H. Yang, B. Khaleghi, U. Mallappa, M. Shah, K. Fan, M. Kang, and T. Rosing
IEEE Journal of Solid-State Circuits (JSSC), 2025
[J.1] Energy-efficient Reconfigurable XGBoost Inference Accelerator with Modular Unit Trees via Selective Node Execution and Data Movement (under review)
C. E. Song, Y. Li, A. Ramnani, P. Agrawal, P. Agrawal, S. Jang, S. Lee, T. Rosing, and M. Kang
IEEE Journal of Solid-State Circuits (JSSC), 2025
<Work Experience>
TSMC Technology, Inc, San Jose, USA
2024.06 - 2024.09 | AI Hardware Research Intern (Manager: Kerem Akarvardar)
- Circuit and architecture co-design for softmax optimization in Large Language Model
<Research Experience>
System Energy Efficiency LAB, UC San Diego, USA
2023.01 - Present | Graduate Student Researcher (Advisor: Prof. Tajana Rosing, http://varys.ucsd.edu/)
- AI Processor design for hyperdimensional computing, few-shot learning, and ReRAM in-memory computing
- Chip fabricated in TSMC N40 (one digital, and one mixed signal), including front-end (RTL design) and back-end (floorplanning, place & route, and gds2)
Vertically-integrated VLSI Information Processing LAB, UC San Diego, USA
2022.09 - Present | Graduate Student Researcher (Advisor: Prof. Mingu Kang, https://ucsdvvip.com/)
- AI Processor design for large language model, and decision tree (XGBoost)
- Digital chip fabricated in TSMC N65, including ML optimization, front-end (RTL design), and back-end (floorplanning, place & route, and gds2)
VLSI Signal Processing LAB, Korea University, South Korea
2021.09 - 2021.12 | Undergraduate Student Researcher (Advisor: Prof. Park, Jong-Sun)
- “Enhanced Stability of 8T and 10T SRAM over 6T SRAM with monte carlo simulation”; Graduation Thesis II;
Nano-scale Integrated Circuits for Excellence LAB, Nanyang Technological University, Singapore
2021.06 - 2021.08 | Undergraduate Student Researcher (Advisor: Prof. Kim, Tony Tae-Hyoung)
- Research on Computing-In-Memory: Analyzed Ambit In-Memory Accelerator & Advanced Computer Architecture
Bio-Application System & IC LAB, Korea University, South Korea
2021.01 - 2021.06 | Undergraduate Student Researcher (Advisor: Prof. Lee, Hyung-Min)
- “Design of Low-Dropout (LDO) Linear Regulator ICs With Advanced Transient Response”; Graduation Thesis I;
<Teaching Experience>
CSE 140, “Components and Design Techniques for Digital Systems”, UC San Diego, USA
2025.01 - 2025.03 | Teaching Assistant (Instructor: Tajana Šimunić Rosing)
<Patent>
[P.2] Changeun Song, “Three-dimensional flash memory with back gate”, US11688462B2, United States, 2023
[P.1] Changeun Song, “Method and System for Providing Virtual Reality Space”, 10-2020-0019618, Republic of Korea, 2020
<Honors & Awards>
Best Doctoral Research Award, Computer Science and Engineering, UCSD, Jun, 2025
ISCA Student Travel Grant Award, Jun, 2025
Samsung Electronics Global Talent Ph.D. Fellowship, Samsung Electronics Memory Division Solution, Oct. 2023
Hackers Family Scholarship, 21st Hackers Bridge Scholarship, Jun. 2022
Fund Scholarship, College of Engineering: Apple & San Francisco Alumni Association of Korea University, Feb. 2022
Seongnam Scholarships, Seongnam City Scholarship Association Mar. 2021
Dean’s List on 2nd Semester of 2020, Korea University, Feb. 2021
Semester High Honors at School of Engineering, Korea University: 2021, 2020, 2017
Top-honor Scholarships at School of Electrical Engineering, Korea University: 2021, 2020, 2017, 2016
<Chip Gallery>
XGBoost Accelerator
in TSMC N65
(CICC'24)
FSL-HDnn
Accelerator
in TSMC N40
(ESSERC'24)
HD (Clo-HDnn) + ReRAM
Accelerator
in TSMC N40
(VLSI'25)