网络物理系统核心的人工智能传感器:从理论到实际应用

D. Pau
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引用次数: 0

摘要

信息物理系统(CPS)在嵌入式系统中正变得越来越普遍。人工智能、机器学习和深度学习大多局限于云端,在那里,无限的计算资源似乎是可用的,并且在不知疲倦地发展。不幸的是,在物联网的预期场景中,将部署数千亿传感器通过低数据速率网络进行通信的分层架构将很快变得过于集中,可扩展性差,响应速度慢。在这种背景下,意法半导体正在开发解决方案,使人工智能更接近传感器。本次演讲将回顾正在开发和公开发布的新的智能技术解决方案和机制,即STM32CUBE.AI。演讲将讲述它们如何代表设计当前和未来一代人工智能网络物理嵌入式系统以及基于意法半导体异构传感器、微控制器和soc的衍生应用所需的关键成分。特别是,将通过基于STM32CUBE.AI的实际示例讨论与如何解决当前互操作性、生产力和受限嵌入式资源差距相关的方面。此外,将介绍适应性和认知计算智能技术的研究和设计,这些技术能够学习,采用人工神经网络,并在非平稳环境中运行。最后,还将讨论能够在时变环境中运行的网络化智能网络物理系统的部署。
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Artificial intelligent sensors at the core of cyber-physical-systems: from theory to practical applications
Cyber-Physical Systems (CPS) are becoming, without pace, more pervasive into embedded systems. Artificial Intelligence, Machine Learning and Deep Learning are mostly confined into the cloud, where unlimited computing resources seems to be available and evolving tirelessly. Unfortunately a layered architecture in which dumb sensors are attached to the cloud would become quickly too centralized, poorly scalable and slowly responsive in the IoT expected scenario that will deploy hundreds of billions of sensors communicating through low data rate networks. In that context, STMicroelectronics is developing solutions to bring Artificial Intelligence closer to the sensors. This talk will review new intelligent technological solutions and mechanisms under development and publicly announced, namely STM32CUBE.AI. The talk will tell how they represent the key ingredients needed to design the current and future generation of artificial intelligent cyber-physical embedded systems and derived applications based on STMicroelectronics heterogeneous sensors, micro controllers and SoCs. In particular, aspects related on how address current interoperability, productivity and constrained embedded resource gaps will be discussed with practical examples based on STM32CUBE.AI. Moreover, the investigation and design of adaptive and cognitive computational-intelligence techniques able to learn, adopting artificial neural networks, and operate in nonstationary environments will be introduced. Finally, the deployment of networked intelligent cyber-physical systems, able to operate in time varying environments, will be also commented.
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