VEDLIoT -- Next generation accelerated AIoT systems and applications
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 27 references that are in the map. First posted on arXiv 9 May 2023.
Builds on
11 of 27 references.
References · 11
- 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,329
- 2018FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networkscited by 56
- 2021Google Neural Network Models for Edge Devices: Analyzing and Mitigating Machine Learning Inference Bottleneckscited by 23
- 2022Attestation Mechanisms for Trusted Execution Environments Demystifiedcited by 8
- 2022WaTZ: A Trusted WebAssembly Runtime Environment with Remote Attestation for TrustZonecited by 6
- 2022A Compositional Approach to Creating Architecture Frameworks with an Application to Distributed AI Systemscited by 4
- 2023An investigation of challenges encountered when specifying training data and runtime monitors for safety critical ML applicationscited by 4
- 2021Twine: An Embedded Trusted Runtime for WebAssemblycited by 3
- 2022WebAssembly as a Common Layer for the Cloud-edge Continuumcited by 3
- 2019LEGaTO: Low-Energy, Secure, and Resilient Toolset for Heterogeneous Computingcited by 2
- 2022VEDLIoT: Very Efficient Deep Learning in IoTcited by 1
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Citing papers per year: 2026: 1.
1 paper.
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