The VEDLIoT project aims to develop energy-efficient Deep Learning methodologies for distributed Artificial Intelligence of Things (AIoT) applications. During our project, we propose a holistic approach that focuses on optimizing algorithms while addressing safety and security challenges inherent to AIoT systems. The foundation of this approach lies in a modular and scalable cognitive IoT hardware platform, which leverages microserver technology to enable users to configure the hardware to meet the requirements of a diverse array of applications. Heterogeneous computing is used to boost performance and energy efficiency. In addition, the full spectrum of hardware accelerators is integrated, providing specialized ASICs as well as FPGAs for reconfigurable computing. The project's contributions span across trusted computing, remote attestation, and secure execution environments, with the ultimate goal of facilitating the design and deployment of robust and efficient AIoT systems. The overall architecture is validated on use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. Ten additional use cases are integrated via an open call, broadening the range of application areas.

翻译：VEDLIoT项目旨在为分布式人工智能物联网（AIoT）应用开发能效优化的深度学习技术。在本项目中，我们提出了一种整体性方法，在优化算法的同时应对AIoT系统固有的安全与可靠性挑战。该方法的基础是一个模块化、可扩展的认知物联网硬件平台，该平台利用微服务器技术，使用户能够根据多样化应用需求配置硬件。异构计算被用于提升性能与能效。此外，平台集成了全谱系硬件加速器，既提供专用集成电路（ASIC），也包含用于可重构计算的可编程门阵列（FPGA）。项目的贡献涵盖可信计算、远程认证及安全执行环境，最终目标是促进稳健且高效的AIoT系统的设计与部署。整体架构在从智能家居到汽车及工业物联网设备的用例中得到验证。通过公开征集，额外集成了十个用例，从而拓展了应用领域范围。