论文标题
Tinyiree：嵌入式系统从编译到部署的ML执行环境
TinyIREE: An ML Execution Environment for Embedded Systems from Compilation to Deployment
论文作者
论文摘要
在过去十年中，用于培训和执行的机器学习模型部署一直是行业和学术研究的重要主题。大部分注意力集中在开发特定的工具链上以支持加速硬件。在本文中，我们提出了IREE，这是一个统一的编译器和运行时堆栈，其明确的目标是将机器学习程序扩展到移动设备和边缘设备的最小足迹，同时保持扩展到更大的部署目标的能力。 IREE采用了基于编译器的方法，并通过使用MLIR编译器基础结构来优化异质硬件加速器，该基础架构提供了快速设计和实施多级编译器中间表示（IR）的手段。更具体地说，本文的重点是Tinyiree，这是IREE中的一组部署选项，可容纳嵌入式系统和裸机平台中的有限的内存和计算资源，同时还展示了IREE的直观工作流，该工作流程为不同的ISA扩展而产生工作负载，并通过LLVM通过LLVM。
Machine learning model deployment for training and execution has been an important topic for industry and academic research in the last decade. Much of the attention has been focused on developing specific toolchains to support acceleration hardware. In this paper, we present IREE, a unified compiler and runtime stack with the explicit goal to scale down machine learning programs to the smallest footprints for mobile and edge devices, while maintaining the ability to scale up to larger deployment targets. IREE adopts a compiler-based approach and optimizes for heterogeneous hardware accelerators through the use of the MLIR compiler infrastructure which provides the means to quickly design and implement multi-level compiler intermediate representations (IR). More specifically, this paper is focused on TinyIREE, which is a set of deployment options in IREE that accommodate the limited memory and computation resources in embedded systems and bare-metal platforms, while also demonstrating IREE's intuitive workflow that generates workloads for different ISA extensions and ABIs through LLVM.
