This paper presents a comprehensive exploration into the integration of Internet of Things(IoT),big data analysis,cloud computing,and Artificial Intelligence(AI),which has led to an unprecedented era of connectivity.W...This paper presents a comprehensive exploration into the integration of Internet of Things(IoT),big data analysis,cloud computing,and Artificial Intelligence(AI),which has led to an unprecedented era of connectivity.We delve into the emerging trend of machine learning on embedded devices,enabling tasks in resource-limited environ-ments.However,the widespread adoption of machine learning raises significant privacy concerns,necessitating the development of privacy-preserving techniques.One such technique,secure multi-party computation(MPC),allows collaborative computations without exposing private inputs.Despite its potential,complex protocols and communication interactions hinder performance,especially on resource-constrained devices.Efforts to enhance efficiency have been made,but scalability remains a challenge.Given the success of GPUs in deep learning,lever-aging embedded GPUs,such as those offered by NVIDIA,emerges as a promising solution.Therefore,we propose an Embedded GPU-based Secure Two-party Computation(EG-STC)framework for Artificial Intelligence(AI)systems.To the best of our knowledge,this work represents the first endeavor to fully implement machine learning model training based on secure two-party computing on the Embedded GPU platform.Our experimental results demonstrate the effectiveness of EG-STC.On an embedded GPU with a power draw of 5 W,our implementation achieved a secure two-party matrix multiplication throughput of 5881.5 kilo-operations per millisecond(kops/ms),with an energy efficiency ratio of 1176.3 kops/ms/W.Furthermore,leveraging our EG-STC framework,we achieved an overall time acceleration ratio of 5–6 times compared to solutions running on server-grade CPUs.Our solution also exhibited a reduced runtime,requiring only 60%to 70%of the runtime of previously best-known methods on the same platform.In summary,our research contributes to the advancement of secure and efficient machine learning implementations on resource-constrained embedded devices,paving the way for broader adoption o展开更多
定位与地图构建(Simultaneous Localization And Mapping,SLAM)技术在机器人、无人机以及虚拟现实等领域有着广泛的应用。然后由于计算资源等条件的限制,在嵌入式系统中应用SLAM技术仍是一种挑战。文章基于嵌入式GPU技术和双目流摄像头...定位与地图构建(Simultaneous Localization And Mapping,SLAM)技术在机器人、无人机以及虚拟现实等领域有着广泛的应用。然后由于计算资源等条件的限制,在嵌入式系统中应用SLAM技术仍是一种挑战。文章基于嵌入式GPU技术和双目流摄像头设计实现了嵌入式实时SLAM系统,并结合深度学习的目标识别技术,来进一步优化环境信息的获取并解决环境认知和自身定位等问题。本应用系统样例,综合应用了人工智能、嵌入式操作系统和嵌入式GPU边缘计算技术,是嵌入式技术课程深入建设发展的重要趋势之一。展开更多
基金supported in part by Major Science and Technology Demonstration Project of Jiangsu Provincial Key R&D Program under Grant No.BE2023025in part by the National Natural Science Foundation of China under Grant No.62302238+2 种基金in part by the Natural Science Foundation of Jiangsu Province under Grant No.BK20220388in part by the Natural Science Research Project of Colleges and Universities in Jiangsu Province under Grant No.22KJB520004in part by the China Postdoctoral Science Foundation under Grant No.2022M711689.
文摘This paper presents a comprehensive exploration into the integration of Internet of Things(IoT),big data analysis,cloud computing,and Artificial Intelligence(AI),which has led to an unprecedented era of connectivity.We delve into the emerging trend of machine learning on embedded devices,enabling tasks in resource-limited environ-ments.However,the widespread adoption of machine learning raises significant privacy concerns,necessitating the development of privacy-preserving techniques.One such technique,secure multi-party computation(MPC),allows collaborative computations without exposing private inputs.Despite its potential,complex protocols and communication interactions hinder performance,especially on resource-constrained devices.Efforts to enhance efficiency have been made,but scalability remains a challenge.Given the success of GPUs in deep learning,lever-aging embedded GPUs,such as those offered by NVIDIA,emerges as a promising solution.Therefore,we propose an Embedded GPU-based Secure Two-party Computation(EG-STC)framework for Artificial Intelligence(AI)systems.To the best of our knowledge,this work represents the first endeavor to fully implement machine learning model training based on secure two-party computing on the Embedded GPU platform.Our experimental results demonstrate the effectiveness of EG-STC.On an embedded GPU with a power draw of 5 W,our implementation achieved a secure two-party matrix multiplication throughput of 5881.5 kilo-operations per millisecond(kops/ms),with an energy efficiency ratio of 1176.3 kops/ms/W.Furthermore,leveraging our EG-STC framework,we achieved an overall time acceleration ratio of 5–6 times compared to solutions running on server-grade CPUs.Our solution also exhibited a reduced runtime,requiring only 60%to 70%of the runtime of previously best-known methods on the same platform.In summary,our research contributes to the advancement of secure and efficient machine learning implementations on resource-constrained embedded devices,paving the way for broader adoption o
文摘定位与地图构建(Simultaneous Localization And Mapping,SLAM)技术在机器人、无人机以及虚拟现实等领域有着广泛的应用。然后由于计算资源等条件的限制,在嵌入式系统中应用SLAM技术仍是一种挑战。文章基于嵌入式GPU技术和双目流摄像头设计实现了嵌入式实时SLAM系统,并结合深度学习的目标识别技术,来进一步优化环境信息的获取并解决环境认知和自身定位等问题。本应用系统样例,综合应用了人工智能、嵌入式操作系统和嵌入式GPU边缘计算技术,是嵌入式技术课程深入建设发展的重要趋势之一。
