177 citations · 358 across the 8 of their papers we have counts for
5 papers · 1 filter
Relax: Composable Abstractions for End-to-End Dynamic Machine Learning
Ruihang Lai, Junru Shao, Siyuan Feng +16
Dynamic shape computations have become critical in modern machine learning workloads, especially in emerging large language models. The success of these models has driven the deman…
WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models
Chenyuan Yang, Yinlin Deng, Runyu Lu +4
Compiler correctness is crucial, as miscompilation can falsify program behaviors, leading to serious consequences. Fuzzing has been studied to uncover compiler defects. However, co…
Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation
Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang +1
Program synthesis has been long studied with recent approaches focused on directly using the power of Large Language Models (LLMs) to generate code. Programming benchmarks, with cu…
NPS: A Framework for Accurate Program Sampling Using Graph Neural Network
Yuanwei Fang, Zihao Liu, Yanheng Lu +7
With the end of Moore's Law, there is a growing demand for rapid architectural innovations in modern processors, such as RISC-V custom extensions, to continue performance scaling.…
NeuRI: Diversifying DNN Generation via Inductive Rule Inference
Jiawei Liu, Jinjun Peng, Yuyao Wang +1
Deep Learning (DL) is prevalently used in various industries to improve decision-making and automate processes, driven by the ever-evolving DL libraries and compilers. The correctn…