Hidet: Task-Mapping Programming Paradigm for Deep Learning Tensor Programs
Yaoyao Ding, Cody Hao Yu, Bojian Zheng, Yizhi Liu, Yida Wang, Gennady Pekhimenko
Abstract
As deep learning models nowadays are widely adopted by both cloud services and edge devices, reducing the latency of deep learning model inferences becomes crucial to provide efficient model serving. However, it is challenging to develop efficient tensor programs for deep learning operators due to the high complexity of modern accelerators (e.g., NVIDIA GPUs and Google TPUs) and the rapidly growing number of operators.
Topics & Concepts
Computer scienceDeep learningCloud computingTask (project management)Artificial intelligenceProgramming paradigmTensor (intrinsic definition)Latency (audio)Enhanced Data Rates for GSM EvolutionComputer architectureProgramming languageOperating systemTelecommunicationsEconomicsManagementMathematicsPure mathematicsParallel Computing and Optimization TechniquesAdvanced Neural Network ApplicationsTensor decomposition and applications