2 citations · 3 across the 3 of their papers we have counts for
5 papers
Axe: A Simple Unified Layout Abstraction for Machine Learning Compilers
Bohan Hou, Hongyi Jin, Guanjie Wang +7
Scaling modern deep learning workloads demands coordinated placement of data and compute across device meshes, memory hierarchies, and heterogeneous accelerators. We present Axe La…
LoRAFusion: Efficient LoRA Fine-Tuning for LLMs
Zhanda Zhu, Qidong Su, Yaoyao Ding +3
Low-Rank Adaptation (LoRA) has become the leading Parameter-Efficient Fine-Tuning (PEFT) method for Large Language Models (LLMs), as it significantly reduces GPU memory usage while…
Aegis: Taxonomy and Optimizations for Overcoming Agent-Environment Failures in LLM Agents
Kevin Song, Anand Jayarajan, Yaoyao Ding +4
Large Language Models (LLMs) agents augmented with domain tools promise to autonomously execute complex tasks requiring human-level intelligence, such as customer service and digit…
Hexcute: A Compiler Framework for Automating Layout Synthesis in GPU Programs
Xiao Zhang, Yaoyao Ding, Bolin Sun +3
Efficient GPU programming is crucial for achieving high performance in deep learning (DL) applications. The performance of GPU programs depends on how data is parallelized across t…
Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation
Yaoyao Ding, Bohan Hou, Xiao Zhang +5
Serving Large Language Models (LLMs) is critical for AI-powered applications, yet it demands substantial computational resources, particularly in memory bandwidth and computational…