Hao (Mark) Chen

Hao (Mark) Chen

PhD candidate at Imperial College London.

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

portfolio

publications

When Monte-Carlo Dropout Meets Multi-Exit: Optimizing Bayesian Neural Networks on FPGA

Design Automation Conference (DAC), 2023

Hongxiang Fan, Hao (Mark) Chen, Liam Castelli, Zhiqiang Que, He Li, Kenneth Long, Wayne Luk
[Paper] [Code]

Web-based AI System for Medical Image Segmentation

Conference on Medical Image Understanding and Analysis (MIUA), 2023

Hao (Mark) Chen, Taowen Liu, Songyun Hu, Leyang Yu, Yiqi Li, Sihan Tao, Jacqueline Lee, Ahmed E. Fetit
[Paper]

Hardware-Aware Neural Dropout Search for Reliable Uncertainty Prediction on FPGA

Design Automation Conference (DAC), 2024

Zehuan Zhang, Hongxiang Fan, Hao (Mark) Chen, Lukasz Dudziak and Wayne Luk
[Paper]

Hardware-Aware Parallel Prompt Decoding for Memory-Efficient Acceleration of LLM Inference

Manuscript

Hao (Mark) Chen, Wayne Luk, Ka Fai Cedric Yiu, Rui Li, Konstantin Mishchenko, Stylianos I. Venieris, Hongxiang Fan
[Paper] [Code]

Algorithm and Hardware Co-Design for Multi-Exit Dropout-based Bayesian Neural Networks

Manuscript

Hao (Mark) Chen, Liam Castelli, Martin Ferianc, Shuanglong Liu, Wayne Luk, Hongxiang Fan
[Paper]

Exploring Code Language Models for Automated HLS-based Hardware Generation: Benchmark, Infrastructure and Analysis

Asia and South Pacific Design Automation Conference (ASP-DAC), 2025

Jiahao Gai, Hao (Mark) Chen, Zhican Wang, Hongyu Zhou, Wanru Zhao, Nicholas Lane, Hongxiang Fan
[Paper]

Enhancing LLM-based Quantum Code Generation with Multi-Agent Optimization and Quantum Error Correction

Design Automation Conference (DAC), 2025

Charlie Campbell, Hao (Mark) Chen, Wayne Luk, Hongxiang Fan
[Paper]

Progressive Mixed-Precision Decoding for Efficient LLM Inference

International Conference on Learning Representations (ICLR), 2025

Hao (Mark) Chen, Fuwen Tan, Alexandros Kouris, Royson Lee, Hongxiang Fan, Stylianos I. Venieris
[Paper] [Code]

Advancing AI-assisted Hardware Design with Hierarchical Decentralized Training and Personalized Inference-Time Optimization

Manuscript

Hao (Mark) Chen, Zehuan Zhang, Wanru Zhao, Nicholas Lane, Wayne Luk, Hongxiang Fan
[Paper]

FW-Merging: Scaling Model Merging with Frank-Wolfe Optimization

Manuscript

Hao (Mark) Chen, Shell Xu Hu, Wayne Luk, Timothy Hospedales, Hongxiang Fan
[Paper]

talks

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