关键词: 数据流模型, 端到端编译, MLIR

Abstract: Thanks to the instruction set architecture (ISA), the software community and the hardware community has been developing independently for years. However, with the advent of multi-core accelerators, the sequential programming model based on the Von Neumann architecture is confronted with troubles. Based on sequential execution model, ISA lacks support for parallel multi-core hardware. Thus, merely using ISA cannot decouple software and hardware. A new program execution model (PXM) is required to accomplish end-to-end compilation from neural networks to interface with sequentially executed programming platforms and parallel multi-core hardware backends, further exploring the optimization opportunities provided by parallel hardware. This paper proposes a codelet model as a new PXM, providing a general abstraction for the process of downloading sequentially executed programs onto parallel hardware. It further decouples the software frontend and hardware backend based on the instruction set. To ensure the reusability of the project, this paper implements the codelet model in the form of a codelet dialect within the MLIR compiler framework proposed by Google. MLIR aims to integrate fragmented compiler ecosystems and improve the reusability of frontend-to-backend integration processes. The codelet model implemented in MLIR in this paper can further enhance the reusability of the MLIR system.

Key words: codelet model, end-to-end compilation, MLIR