Mufeng Chen

Mufeng Chen

Zhejiang University

Publications

Analog Multilevel eDRAM-RRAM CIM for Zeroth-Order Fine-tuning of LLMs

Published in imw 25, 2025

This paper mainly provide design methodology for fused CiM.

Recommended citation: M. Chen, L. Zheng, J. -Y. Lin, P. D. Ye and H. Li, "Analog Multilevel eDRAM-RRAM CIM for Zeroth-Order Fine-tuning of LLMs," 2025 IEEE International Memory Workshop (IMW), Monterey, CA, USA, 2025, pp. 1-4, doi: 10.1109/IMW61990.2025.11026966. keywords: {Large language models;Field effect transistors;Metals;Programming;In-memory computing;Common Information Model (computing);Silicon;Reliability;Optimization;Method of moments;Compute-in-memory (CIM);eDRAM;RRAM;MLC;oxide semiconductors;LLM fine-tuning},

An Electromagnetic Perspective of Artificial Intelligence Neuromorphic Chips

Published in Electromagnetic Science, 2023

This paper mainly gives an overview of the CiM design and signal integrity problem.

Recommended citation: Er-Ping Li, Hanzhi Ma, Manareldeen Ahmed, et al., “An Electromagnetic Perspective of Artificial Intelligence Neuromorphic Chips,” Electromagnetic Science, vol. 1, no. 3, article no. 0030151, 2023. doi: 10.23919/emsci.2023.0015 https://www.emscience.org/en/article/doi/10.23919/emsci.2023.0015

MAP-SNN: Mapping Spike Activities with Multiplicity, Adaptability, and Plasticity into Bio-Plausible Spiking Neural Networks

Published in Front. Neurosci., 2022

This paper analyze the time-discretion problem in SNN and the bio-plausibility

Recommended citation: Yu C, Du Y, Chen M, Wang A, Wang G and Li E (2022) MAP-SNN: Mapping spike activities with multiplicity, adaptability, and plasticity into bio-plausible spiking neural networks. Front. Neurosci. 16:945037. doi: 10.3389/fnins.2022.945037 https://www.frontiersin.org/articles/10.3389/fnins.2022.945037/