移动互联网信息无障碍(mobile Internet information accessibility,MIIA)旨在确保移动应用内容对所有用户(包括视障人士等)都能平等、便捷、无障碍地获取和使用.系统综述移动互联网信息无障碍领域的最新研究进展,重点分析总结移动端GUI...移动互联网信息无障碍(mobile Internet information accessibility,MIIA)旨在确保移动应用内容对所有用户(包括视障人士等)都能平等、便捷、无障碍地获取和使用.系统综述移动互联网信息无障碍领域的最新研究进展,重点分析总结移动端GUI(graphical user interface)语义表征与理解、无障碍检测以及布局修复等方面的研究成果.分析表明,从传统启发式规则方法到深度学习驱动的自动化工具,相关技术逐渐提升了检测的精度和适应性,同时也揭示了在应对复杂动态交互和多样化用户需求方面的挑战,并对未来研究方向进行了展望.移动互联网信息无障碍技术已显著改善了视障用户的数字体验,但仍需不断创新与优化,以实现真正普惠与包容的数字社会.展开更多
There are a wide variety of intelligence accelerators with promising performance and energy efficiency,deployed in a broad range of applications such as computer vision and speech recognition.However,programming produ...There are a wide variety of intelligence accelerators with promising performance and energy efficiency,deployed in a broad range of applications such as computer vision and speech recognition.However,programming productivity hinders the deployment of deep learning accelerators.The low-level library invoked in the high-level deep learning framework which supports the end-to-end execution with a given model,is designed to reduce the programming burden on the intelligence accelerators.Unfortunately,it is inflexible for developers to build a network model for every deep learning application,which probably brings unnecessary repetitive implementation.In this paper,a flexible and efficient programming framework for deep learning accelerators,FlexPDA,is proposed,which provides more optimization opportunities than the low-level library and realizes quick transplantation of applications to intelligence accelerators for fast upgrades.We evaluate FlexPDA by using 10 representative operators selected from deep learning algorithms and an end-to-end network.The experimental results validate the effectiveness of FlexPDA,which achieves an end-to-end performance improvement of 1.620x over the low-level library.展开更多
文摘移动互联网信息无障碍(mobile Internet information accessibility,MIIA)旨在确保移动应用内容对所有用户(包括视障人士等)都能平等、便捷、无障碍地获取和使用.系统综述移动互联网信息无障碍领域的最新研究进展,重点分析总结移动端GUI(graphical user interface)语义表征与理解、无障碍检测以及布局修复等方面的研究成果.分析表明,从传统启发式规则方法到深度学习驱动的自动化工具,相关技术逐渐提升了检测的精度和适应性,同时也揭示了在应对复杂动态交互和多样化用户需求方面的挑战,并对未来研究方向进行了展望.移动互联网信息无障碍技术已显著改善了视障用户的数字体验,但仍需不断创新与优化,以实现真正普惠与包容的数字社会.
基金This work was supported by the National Key Research and Development Program of China under Grant No.2017YFB1003103the Natural Science Research Foundation of Jilin Province of China under Grant No.20190201193JCthe Fundamental Research Funds for the Central Universities,JLU.
文摘There are a wide variety of intelligence accelerators with promising performance and energy efficiency,deployed in a broad range of applications such as computer vision and speech recognition.However,programming productivity hinders the deployment of deep learning accelerators.The low-level library invoked in the high-level deep learning framework which supports the end-to-end execution with a given model,is designed to reduce the programming burden on the intelligence accelerators.Unfortunately,it is inflexible for developers to build a network model for every deep learning application,which probably brings unnecessary repetitive implementation.In this paper,a flexible and efficient programming framework for deep learning accelerators,FlexPDA,is proposed,which provides more optimization opportunities than the low-level library and realizes quick transplantation of applications to intelligence accelerators for fast upgrades.We evaluate FlexPDA by using 10 representative operators selected from deep learning algorithms and an end-to-end network.The experimental results validate the effectiveness of FlexPDA,which achieves an end-to-end performance improvement of 1.620x over the low-level library.
