From the 1 of 10 linked papers with an AI index.
10 papers
Correct but Slow: An Empirical Study of the GPU Kernel Evaluation Gap in Modern Domain-Specific Languages
Tingxi Li, Ravishka Rathnasuriya, Wei Yang
The paper empirically investigates why GPU kernels written in modern DSLs like Triton and TileLang can be functionally correct yet dramatically slower than library implementations,…
Characterizing Real-World Bugs in Tile Programs for Automated Bug Detection
Ravishka Rathnasuriya, Zihe Song, Nidhi Majoju +4
Tile-based programming frameworks are increasingly adopted to write high-performance GPU kernels in domains such as deep learning and scientific computing. While these frameworks e…
When to Answer and When to Defer: A Decision Framework for Reliable Code Predictions
Ravishka Rathnasuriya, Wei Yang
Code language models are increasingly adopted for both understanding and generative tasks. Despite their success, these models frequently produce overconfident incorrect prediction…
On-the-Fly Input Adaptation for Reliable Code Intelligence
Ravishka Rathnasuriya, Wei Yang
Code language models (CLMs) play a central role in software engineering across both generation and classification tasks. However, these models still exhibit notable mispredictions…
AESOP: Adversarial Execution-path Selection to Overload Deep Learning Pipelines
Tingxi Li, Mingfang Ji, Ravishka Shemal Rathnasuriya +3
Modern machine learning deployments increasingly compose specialized models into dynamic inference pipelines, where upstream components produce intermediate predictions that determ…
Framework for On the Fly Input Refinement for Deep Learning Models
Ravishka Rathnasuriya
Advancements in deep learning have significantly improved model performance across tasks involving code, text, and image processing. However, these models still exhibit notable mis…