新一代深度学习框架研究

于璠

doi:10.11959/j.issn.2096-0271.2020034

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新一代深度学习框架研究

专题：大数据异构并行系统 | 更新时间：2024-06-03

- 新一代深度学习框架研究
- Research on the next-generation deep learning framework
- 大数据 2020年6卷第4期页码：2020034-1
- 作者机构：
- 作者简介：
  
  [ "于璠（1980- ），男，博士，华为技术有限公司AI计算框架的创新和生态架构师，曾主导华为云计算资源调度、SDN大规模路由等架构和算法的设计，发表专利30余篇" ]
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2020034
  中图分类号： TP31
- 网络首发：2020-07，
  
  纸质出版：2020-07-15
- 稿件说明：
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于璠. 新一代深度学习框架研究[J]. 大数据, 2020,6(4):2020034-1.

Fan YU. Research on the next-generation deep learning framework[J]. Big Data Research, 2020, 6(4): 2020034-1.
于璠. 新一代深度学习框架研究[J]. 大数据, 2020,6(4):2020034-1. DOI： 10.11959/j.issn.2096-0271.2020034.

Fan YU. Research on the next-generation deep learning framework[J]. Big Data Research, 2020, 6(4): 2020034-1. DOI： 10.11959/j.issn.2096-0271.2020034.

摘要

从人工智能的历史出发，简述深度学习发展历程以及目前的挑战，通过介绍新一代深度学习框架的特点，分析总体框架，阐述自动并行、自动微分、自动调优等技术优势以及协同昇腾处理器的性能优势，希望可以为深度学习技术研究人员提供参考。

Abstract

Started from the history of AI

the development and challenges of deep learning were described

the features of the nextgeneration deep learning framework was introduced

the overall framework was analyzed

and the technical advantages of auto parallel

auto differentiation

automatic tuning

as well as the performance advantages of collaborating with Ascend processors were expanded.This article can be used as a reference for deep learning technology researchers.

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references

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