13 papers
FSGen: Agile Fused and Sparse Accelerator Generator with Accurate Power Model for LLM Applications
Jay Zhe-An Mok, Qijun Zhang, Zhiyao Xie
With the growing demand of artificial intelligence (AI) applications, large language models (LLMs) have become important workloads in many domains. The question of how to efficient…
G-Power: Architecture-level GPU Power Modeling with Aggregated Knowledge Foundations from Known GPUs
Qijun Zhang, Yao Lu, Shang Liu +4
Graphics Processing Units (GPUs) have been serving as critical computation resources for large-scale parallel computations. With increasing chip complexity, power efficiency has be…
ICP: Exploiting Instruction Correlation for Prefetching Irregular Memory Accesses
Mengming Li, Chenlu Miao, Buqing Xu +7
Irregular memory accesses pose challenges for effective and efficient data prefetching. While temporal prefetchers have recently shown promise for irregular memory access patterns,…
MoE-Hub: Taming Software Complexity for Seamless MoE Overlap with Hardware-Accelerated Communication on Multi-GPU Systems
Zhuoshan Zhou, Chen Zhang, Shuyi Zhang +10
The Mixture-of-Experts (MoE) architecture is crucial for scaling large language models, but its scalability is severely limited by inter-GPU communication bottlenecks in multi-GPU…
Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs
Qijun Zhang, Chen Zhang, Zhuoshan Zhou +10
Mixture-of-Experts (MoE) has been adopted by many leading large models to reduce computational requirements. However, frequent inter-GPU communication in MoE expert parallelism (EP…
A New Benchmark for the Appropriate Evaluation of RTL Code Optimization
Yao Lu, Shang Liu, Hangan Zhou +3
The rapid progress of artificial intelligence increasingly relies on efficient integrated circuit (IC) design. Recent studies have explored the use of large language models (LLMs)…