Constructing an AI Compiler for ARM Cortex-M Devices  

作　　者：Rong-Guey Chang Tam-Van Hoang 

机构地区：[1]Department of Computer Science and Information Engineering,National Chung Cheng University,Chia-Yi,Taiwan

出　　处：《Computer Systems Science & Engineering》2023年第7期999-1019,共21页计算机系统科学与工程（英文）

基　　金：This work was supported in part by the Ministry of Science and Technology of Taiwan,R.O.C.,the Grant Number of project 108-2218-E-194-007.

摘　　要：The diversity of software and hardware forces programmers to spend a great deal of time optimizing their source code,which often requires specific treatment for each platform.The problem becomes critical on embedded devices,where computational and memory resources are strictly constrained.Compilers play an essential role in deploying source code on a target device through the backend.In this work,a novel backend for the Open Neural Network Compiler(ONNC)is proposed,which exploits machine learning to optimize code for the ARM Cortex-M device.The backend requires minimal changes to Open Neural Network Exchange(ONNX)models.Several novel optimization techniques are also incorporated in the backend,such as quantizing the ONNX model’s weight and automatically tuning the dimensions of operators in computations.The performance of the proposed framework is evaluated for two applications:handwritten digit recognition on the Modified National Institute of Standards and Technology(MNIST)dataset and model,and image classification on the Canadian Institute For Advanced Research and 10(CIFAR-10)dataset with the AlexNet-Light model.The system achieves 98.90%and 90.55%accuracy for handwritten digit recognition and image classification,respectively.Furthermore,the proposed architecture is significantly more lightweight than other state-of-theart models in terms of both computation time and generated source code complexity.From the system perspective,this work provides a novel approach to deploying direct computations from the available ONNX models to target devices by optimizing compilers while maintaining high efficiency in accuracy performance.

关 键 词：Open neural network compiler backend ARM Cortex-M device handwritten digit recognition image classification 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]