Hardware-Aware Neural Dropout Search for Reliable Uncertainty Prediction on FPGA. - Zehuan Zhang; Hongxiang Fan; Hao Mark Chen; Lukasz Dudziak; Wayne Luk (DAC, 2024)

Hardware-Aware Parallel Prompt Decoding for Memory-Efficient Acceleration of LLM Inference. - Hao Mark Chen; Wayne Luk; Ka Fai Cedric Yiu; Rui Li 052; Konstantin Mishchenko; Stylianos I. Venieris; Hongxiang Fan (CoRR, 2024)

Enhancing Dropout-based Bayesian Neural Networks with Multi-Exit on FPGA. - Hao Mark Chen; Liam Castelli; Martin Ferianc; Hongyu Zhou; Shuanglong Liu; Wayne Luk; Hongxiang Fan (CoRR, 2024)

Progressive Mixed-Precision Decoding for Efficient LLM Inference. - Hao Mark Chen; Fuwen Tan; Alexandros Kouris; Royson Lee; Hongxiang Fan; Stylianos I. Venieris (ICLR, 2025)

FW-Merging: Scaling Model Merging with Frank-Wolfe Optimization. - Hao Mark Chen; Shell Xu Hu; Wayne Luk; Timothy M. Hospedales; Hongxiang Fan (CoRR, 2025)

Enhancing LLM-based Quantum Code Generation with Multi-Agent Optimization and Quantum Error Correction. - Charlie Campbell; Hao Mark Chen; Wayne Luk; Hongxiang Fan (CoRR, 2025)

Rethinking Optimal Verification Granularity for Compute-Efficient Test-Time Scaling. - Hao Mark Chen; Guanxi Lu; Yasuyuki Okoshi; Zhiwen Mo; Masato Motomura; Hongxiang Fan (CoRR, 2025)

Advancing AI-assisted Hardware Design with Hierarchical Decentralized Training and Personalized Inference-Time Optimization. - Hao Mark Chen; Zehuan Zhang; Wanru Zhao; Nicholas D. Lane; Hongxiang Fan (CoRR, 2025)