ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2020, Vol. 57 ›› Issue (9): 1839-1853.doi: 10.7544/issn1000-1239.2020.20200291

所属专题: 2020边缘计算专题

• 网络技术 • 上一篇    下一篇

TensorFlow Lite:端侧机器学习框架

李双峰   

  1. (Google TensorFlow团队 北京 100190) (shuangfeng@google.com)
  • 出版日期: 2020-09-01

TensorFlow Lite: On-Device Machine Learning Framework

Li Shuangfeng   

  1. (Google TensorFlow Team, Beijing 100190)
  • Online: 2020-09-01

摘要: TensorFlow Lite(TFLite)是一个轻量、快速、跨平台的专门针对移动和IoT场景的开源机器学习框架,是TensorFlow的一部分,支持安卓、iOS、嵌入式Linux以及MCU等多个平台部署.它大大降低开发者使用门槛,加速端侧机器学习的发展,推动机器学习无处不在.介绍了端侧机器学习的浪潮、挑战和典型应用;TFLite的起源和系统架构;TFLite的最佳实践,以及适合初学者的工具链;展望了未来的发展方向.

关键词: 机器学习, 端侧机器学习, TensorFlow, TensorFlow Lite, TFLite, 移动, 物联网

Abstract: TensorFlow Lite (TFLite) is a lightweight, fast and cross-platform open source machine learning framework specifically designed for mobile and IoT. It’s part of TensorFlow and supports multiple platforms such as Android, iOS, embedded Linux, and MCU etc. It greatly reduces the barrier for developers, accelerates the development of on-device machine learning (ODML), and makes ML run everywhere. This article introduces the trend, challenges and typical applications of ODML; the origin and system architecture of TFLite; best practices and tool chains suitable for ML beginners; and the roadmap of TFLite.

Key words: machine learning, on-device machine learning (ODML), TensorFlow, TensorFlow Lite, TFLite, mobile, IoT

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