A comprehensive study of deep learning compiler bugs
Qingchao Shen, Haoyang Ma, Junjie Chen, Yongqiang Tian, Shing-Chi Cheung, Xiang Chen
Abstract
There are increasing uses of deep learning (DL) compilers to generate optimized code, boosting the runtime performance of DL models on specific hardware. Like their traditional counterparts, DL compilers can generate incorrect code, resulting in unexpected model behaviors that may cause catastrophic consequences in mission-critical systems. On the other hand, the DL models processed by DL compilers differ fundamentally from imperative programs in that the program logic in DL models is implicit. As such, various characteristics of the bugs arising from traditional compilers need to be revisited in the context of DL compilers.
Topics & Concepts
CompilerComputer scienceProgramming languageBoosting (machine learning)Deep learningContext (archaeology)Code (set theory)Code generationParallel computingArtificial intelligenceOperating systemSet (abstract data type)PaleontologyKey (lock)BiologySoftware Testing and Debugging TechniquesSoftware Reliability and Analysis ResearchSoftware Engineering Research