Fanchen Kong

Computer Architecture

AI Accelerator

Network On Chip

Fanchen Kong

Phd student

Hardware-efficient AI and ML

+32495841434

fanchen.kong@esat.kuleuven.be

Marian Verhelst

Or meet the full MICAS team

Current research topic

XDMA: A Distributed DMA for Flexible and Efficient Data Movement in Heterogeneous Multi-Accelerator SoCs - Marian Verhelst
Uncertainty-Aware Design Space Exploration for AI Accelerators - Georges Gielen, Marian Verhelst

Career overview

Fanchen Kong was born in Shanxi, China in 1997.

He received MSEE @ KU Leuven with Magna Cum Laude in 2023. His master's thesis is "Scalable Integration of Multi-Core Deep Learning Accelerators via NoC and Network-Aware Workload Distribution".

Currently, he works as a research assistant at the ESAT-MICAS under the guidance of Prof. Marian Verhelst. His research focuses on building efficient and scalable multi-core AI accelerator systems.

Publications

< /i>FlexiGen< //i>: An Automated AI Accelerator Generation Framework With Decoupled-Access-Execute and Dynamic Dataflows Xiaoling Yi, Man Shi, Joren Dumoulin, Jiacong Sun, Yunhao Deng, Ryan Antonio, Fanchen Kong, Yu Zhang, Yuqi Jiang, and Marian Verhelst · Article · Mar 4. 2026 IEEE Transactions On Circuits And Systems I-Regular Papers; 2026

Awards & honors

2024 Best Master Thesis from ICsense