3 citations · 8 across the 15 of their papers we have counts for
8 papers · 1 filter
Enhancing Trustworthiness with Mixed Precision: Benchmarks, Opportunities, and Challenges
Guanxi Lu, Hao Mark Chen, Zhiqiang Que +2
Large language models (LLMs) have shown promising performance across various tasks. However, their autoregressive decoding process poses significant challenges for efficient deploy…
AdaBlock-dLLM: Semantic-Aware Diffusion LLM Inference via Adaptive Block Size
Guanxi Lu, Hao Mark Chen, Yuto Karashima +3
Diffusion-based large language models (dLLMs) are gaining attention for their inherent capacity for parallel decoding, offering a compelling alternative to autoregressive LLMs. Amo…
FastTTS: Accelerating Test-Time Scaling for Edge LLM Reasoning
Hao Mark Chen, Zhiwen Mo, Guanxi Lu +4
Recent advances in reasoning Large Language Models (LLMs) are driving the emergence of agentic AI systems. Edge deployment of LLM agents near end users is increasingly necessary to…
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…
Enhancing LLM-based Quantum Code Generation with Multi-Agent Optimization and Quantum Error Correction
Charlie Campbell, Hao Mark Chen, Wayne Luk +1
Multi-agent frameworks with Large Language Models (LLMs) have become promising tools for generating general-purpose programming languages using test-driven development, allowing de…
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…
FW-Merging: Scaling Model Merging with Frank-Wolfe Optimization
Hao Mark Chen, Shell Xu Hu, Wayne Luk +2
Model merging has emerged as a promising approach for multi-task learning (MTL), offering a data-efficient alternative to conventional fine-tuning. However, with the rapid developm…