Hao (Mark) Chen

Hao (Mark) Chen

PhD candidate at Imperial College London.

Publications

You can also find the exhaustive list of my articles on my Google Scholar profile.

Rethinking Optimal Verification Granularity for Compute-Efficient Test-Time Scaling

2025 The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS)

Hao Mark Chen, Guanxi Lu, Yasuyuki Okoshi, Zhiwen Mo, Masato Motomura, Hongxiang Fan
[Paper]

FW-Merging: Scaling model merging with frank-wolfe optimization

2025 International Conference on Computer Vision (ICCV)

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

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

Proceedings of the 30th Asia and South Pacific Design Automation Conference (ASP-DAC)

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

2025 62th ACM/IEEE Design Automation Conference (DAC)

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

Democratizing Agentic AI with Fast Test-Time Scaling on the Edge

arXiv preprint arXiv:2509.00195

Hao Mark Chen, Zhiwen Mo, Guanxi Lu, Shuang Liang, Lingxiao Ma, Wayne Luk, Hongxiang Fan
[Paper]

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

arXiv preprint arXiv:2506.00002

Hao Mark Chen, Zehuan Zhang, Wanru Zhao, Nicholas Lane, Hongxiang Fan
[Paper]

AdaBlock-dLLM: Semantic-Aware Diffusion LLM Inference via Adaptive Block Size

arXiv preprint arXiv:2509.26432

Guanxi Lu, Hao Mark Chen, Yuto Karashima, Zhican Wang, Daichi Fujiki, Hongxiang Fan
[Paper]

Progressive Mixed-Precision Decoding for Efficient LLM Inference

2025 The Thirteenth International Conference on Learning Representations (ICLR)

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

Parallel Prompt Decoding: A Cost-Effective and Memory-Efficient Approach for Accelerating LLM Inference

Arxiv

Hao Mark Chen, Wayne Luk, Hongxiang Fan, Roberto Bondesan
[Paper]

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

2025 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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

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

Proceedings of the 61st ACM/IEEE Design Automation Conference

Zehuan Zhang, Hongxiang Fan, Hao Chen, Lukasz Dudziak, Wayne Luk
[Paper]

Enhancing dropout-based Bayesian neural networks with multi-exit on FPGA

arXiv preprint arXiv:2406.14593

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

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

2023 60th ACM/IEEE Design Automation Conference (DAC)

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

Web-Based AI System for Medical Image Segmentation

Medical Image Understanding and Analysis: 27th Annual Conference, MIUA 2023, Aberdeen, UK, July 19–21, 2023, Proceedings

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