1 citations · 2 across the 7 of their papers we have counts for
10 papers
AOSpec: Action and Observation Co-Speculation for Low-Latency Agent Serving
Hao Mark Chen, Jinnan Guo, Wayne Luk +1
Large language model agents increasingly act through stateful tools, yet model generation and environment execution remain serialized at every step. As decoding accelerates, tool e…
Context Memorization for Efficient Long Context Generation
Yasuyuki Okoshi, Hao Mark Chen, Guanxi Lu +3
Modern large language model (LLM) applications increasingly rely on long conditioning prefixes to control model behavior at inference time. While prefix-augmented inference is effe…
DeepStack: Facilitating Co-Design Exploration of 3D DRAM-Stacked Accelerators for Distributed LLM Inference
Zhiwen Mo, Guoyu Li, Hao Mark Chen +11
Advances in hybrid bonding and packaging have driven growing interest in 3D DRAM-stacked AI accelerators. As large language models (LLMs) scale to hundreds of billions or trillions…
Dynamic Expert Sharing: Decoupling Memory from Parallelism in Mixture-of-Experts Diffusion LLMs
Hao Mark Chen, Zhiwen Mo, Royson Lee +6
Among parallel decoding paradigms, diffusion large language models (dLLMs) have emerged as a promising candidate that balances generation quality and throughput. However, their int…
CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics
Wanru Zhao, Hongxiang Fan, Shell Xu Hu +3
Recent research has highlighted the importance of data quality in scaling large language models (LLMs). However, automated data quality control faces unique challenges in collabora…
Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages
Wanru Zhao, Yihong Chen, Royson Lee +4
Pre-trained large language models (LLMs) have become a cornerstone of modern natural language processing, with their capabilities extending across a wide range of applications and…
Advancing AI-assisted Hardware Design with Hierarchical Decentralized Training and Personalized Inference-Time Optimization
Hao Mark Chen, Zehuan Zhang, Wanru Zhao +2
Recent years have witnessed a significant increase in the adoption of AI techniques to enhance electronic design automation. In particular, the emergence of Large Language Models (…
Rethinking Optimal Verification Granularity for Compute-Efficient Test-Time Scaling
Hao Mark Chen, Guanxi Lu, Yasuyuki Okoshi +3
Test-time scaling (TTS) has proven effective in enhancing the reasoning capabilities of large language models (LLMs). Verification plays a key role in TTS, simultaneously influenci…
Exploring Code Language Models for Automated HLS-based Hardware Generation: Benchmark, Infrastructure and Analysis
Jiahao Gai, Hao Mark Chen, Zhican Wang +4
Recent advances in code generation have illuminated the potential of employing large language models (LLMs) for general-purpose programming languages such as Python and C++, openin…
Progressive Mixed-Precision Decoding for Efficient LLM Inference
Hao Mark Chen, Fuwen Tan, Alexandros Kouris +3
In spite of the great potential of large language models (LLMs) across various tasks, their deployment on resource-constrained devices remains challenging due to their excessive co…