Mufeng Chen

West Lafayette, IN, USA

chen5240 [at] purdue.edu

I am a Ph.D. student in Electrical and Computer Engineering at Purdue University, advised by Prof. Haitong Li in the NanoX Lab. My current research focuses on GPU and heterogeneous memory-system architecture for AI and LLM workloads, within the broader theme of cross-layer heterogeneous memory systems for AI and optimization.

Research areas

I study how memory-hierarchy design and data movement shape the efficiency of AI and LLM systems. My ongoing work explores high-bandwidth flash within heterogeneous memory systems for GPU-based AI workloads. At the cache level, my GPU architecture project explores register-file caching and warp scheduling.

This systems focus builds on my research in memory technologies and circuit–architecture co-design. At the domain-specific accelerator level, my published work includes my first-author paper on analog multilevel eDRAM–RRAM CIM for LLM fine-tuning, the CENTAUR floating-point CIM engine, the PROTEUS programmable CIM accelerator, and heterogeneous CIM fabrics for edge inference and learning. Explore the research overview.

Before Purdue, I conducted research at Zhejiang University under Prof. Er-Ping Li. I completed summer research internships at Rice University’s SIMS Lab with Prof. Kaiyuan Yang in 2022 and Purdue’s NanoX Lab with Prof. Haitong Li in 2023. I earned a B.Eng. in Optical and Electronic Information Engineering from Huazhong University of Science and Technology.

news

Aug 01, 2026	PROTEUS, our 40 nm programmable general-purpose digital CiM accelerator, is available in early access at IEEE JSSC. [paper]
Apr 01, 2026	Presented Heterogeneous Compute-in-Memory Fabrics for Efficient, Scalable Edge Inference and Learning at ISQED 2026. [paper]
Nov 01, 2025	CENTAUR, a 38.5-TFLOPS/W floating-point digital CiM engine, presented at IEEE A-SSCC 2025. [paper]
May 21, 2025	Presented Analog Multilevel eDRAM-RRAM CIM for Zeroth-Order Fine-tuning of LLMs at IEEE IMW 2025 in Monterey, CA. [paper]
Aug 19, 2024	Started my Ph.D. in Electrical and Computer Engineering at Purdue University, joining the NanoX Lab.

selected publications

JSSC
PROTEUS: A 40 nm Programmable General-Purpose Digital Compute-In-Memory Accelerator With eNVM and Hierarchical ISA for Versatile Edge AI

Luqi Zheng, Amir Massah Bavani, Mufeng Chen, and 11 more authors

IEEE Journal of Solid-State Circuits, 2026

Early Access

Abs DOI Bib HTML PDF

PROTEUS is an 18 mm2 programmable general-purpose digital compute-in-memory (GP-DCIM) accelerator integrating 4 Mb RRAM and 2.6 Mb tensor SRAM with a 32-bit hierarchical DCIM instruction set architecture, fabricated in 40 nm ultra-low-power CMOS with foundry RRAM. Fine-grained 1-D matrix tiling and a reconfigurable DCIM datapath/pipeline achieve near-100% memory utilization across INT8/INT16/FP8/FP16 computations. PROTEUS unifies SRAM/RRAM dataflows and embeds nonvolatile micro-programs in RRAM, enabling rapid switching among pre-stored kernels without off-chip instruction feeds or RRAM rewrites. The chip delivers 702 GOPS throughput, 6.4 TOPS/W energy efficiency, and 0.039 TOPS/mm2 compute density, validated on ResNet-20, BERT-Tiny, MobileViT, GraphSAGE, and Vision Mamba.
@article{zheng2026proteus, title = {PROTEUS: A 40 nm Programmable General-Purpose Digital Compute-In-Memory Accelerator With eNVM and Hierarchical ISA for Versatile Edge AI}, author = {Zheng, Luqi and Bavani, Amir Massah and Chen, Mufeng and Du, Shuting and Hsin, Te-Yu and Khwa, Win-San and Wu, Ping-Sheng and Lele, Ashwin Sanjay and Zhang, Bo and Crafton, Brian and Chuang, Harry and Chih, Yu-Der and Chang, Meng-Fan and Li, Haitong}, journal = {IEEE Journal of Solid-State Circuits}, year = {2026}, note = {Early Access}, doi = {10.1109/JSSC.2026.3694285}, }
IMW
Analog Multilevel eDRAM-RRAM CIM for Zeroth-Order Fine-tuning of LLMs

Mufeng Chen, Luqi Zheng, Jheng-Yu Lin, and 2 more authors

In 2025 IEEE International Memory Workshop (IMW), 2025

Abs DOI Bib HTML PDF

Zeroth-order fine-tuning eliminates explicit back-propagation and reduces memory overhead for large language models, making it a promising approach for on-device fine-tuning. However, existing memory-centric accelerators fail to fully leverage these benefits due to inefficiencies in balancing bit density, compute-in-memory capability, and the endurance-retention trade-off. We present a reliability-aware, analog multi-level-cell eDRAM-RRAM compute-in-memory solution co-designed with zeroth-order optimization for language model fine-tuning. An RRAM-assisted eDRAM MLC programming scheme is developed, along with a PVT-robust, large-sensing-window time-to-digital converter. The MLC-eDRAM integrating two-finger MOM provides 12x improvement in bit density over state-of-the-art MLC designs, with another 5x density and 2x retention benefit from BEOL In2O3 FETs.
@inproceedings{chen2025analog, title = {Analog Multilevel eDRAM-RRAM CIM for Zeroth-Order Fine-tuning of LLMs}, author = {Chen, Mufeng and Zheng, Luqi and Lin, Jheng-Yu and Ye, Peide D. and Li, Haitong}, booktitle = {2025 IEEE International Memory Workshop (IMW)}, pages = {1--4}, year = {2025}, doi = {10.1109/IMW61990.2025.11026966}, }