Hao Mark Chen

According to our database, Hao Mark Chen authored at least 16 papers between 2024 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

DeepStack: Scalable and Accurate Design Space Exploration for Distributed 3D-Stacked AI Accelerators.

Zhiwen Mo, Guoyu Li, Hao Mark Chen, Yu Cheng, Zhengju Tang, Qianzhou Wang, Lei Wang, Shuang Liang, Lingxiao Ma, Xianqi Zhou, Yuxiao Guo, Wayne Luk, Jilong Xue, Hongxiang Fan

CoRR, April, 2026

Dynamic Expert Sharing: Decoupling Memory from Parallelism in Mixture-of-Experts Diffusion LLMs.

Hao Mark Chen, Zhiwen Mo, Royson Lee, Qianzhou Wang, Da Li, Shell Xu Hu, Wayne Luk, Timothy M. Hospedales, Hongxiang Fan

CoRR, February, 2026

FastTTS: Accelerating Test-Time Scaling for Edge LLM Reasoning.

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

Proceedings of the 31st ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2026

Enhancing Trustworthiness with Mixed Precision: Benchmarks, Opportunities, and Challenges.

Guanxi Lu, Hao Mark Chen, Zhiqiang Que, Wayne Luk, Hongxiang Fan

Proceedings of the 31st Asia and South Pacific Design Automation Conference, 2026

Hardware-Efficient Union-Find Decoder Towards Scalable Topological Quantum Codes.

Shuang Liang, Jubo Xu, Yuncheng Lu, Hao Mark Chen, Bo Yuan, Hongxiang Fan

Proceedings of the 31st Asia and South Pacific Design Automation Conference, 2026

2025

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

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

CoRR, September, 2025

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

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

CoRR, September, 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, June, 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, May, 2025

Progressive Mixed-Precision Decoding for Efficient LLM Inference.

Hao Mark Chen, Fuwen Tan, Alexandros Kouris, Royson Lee, Hongxiang Fan, Stylianos I. Venieris

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

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

Hao Mark Chen, Shell Xu Hu, Wayne Luk, Timothy M. Hospedales, Hongxiang Fan

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2025

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

Hao Mark Chen, Wayne Luk, Yiu Ka Fai Cedric, Rui Li, Konstantin Mishchenko, Stylianos I. Venieris, Hongxiang Fan

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2025, 2025

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

Charlie Campbell, Hao Mark Chen, Wayne Luk, Hongxiang Fan

Proceedings of the 62nd ACM/IEEE Design Automation Conference, 2025

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

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

Hao Mark Chen, Wayne Luk, Ka Fai Cedric Yiu, Rui Li, Konstantin Mishchenko, Stylianos I. Venieris, Hongxiang Fan

CoRR, 2024

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

Zehuan Zhang, Hongxiang Fan, Hao Mark Chen, Lukasz Dudziak, Wayne Luk

Proceedings of the 61st ACM/IEEE Design Automation Conference, 2024