# VEDLIoT -- Next generation accelerated AIoT systems and applications

arXiv: https://arxiv.org/abs/2305.05388 · HTML: https://mlsanity.com/paper/2305.05388/

## Key facts

- Cited by 1 papers 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 (most cited first, 11 of 11)

- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks](https://mlsanity.com/paper/1809.04570/) (2018)
- [Google Neural Network Models for Edge Devices: Analyzing and Mitigating Machine Learning Inference Bottlenecks](https://mlsanity.com/paper/2109.14320/) (2021)
- [Attestation Mechanisms for Trusted Execution Environments Demystified](https://mlsanity.com/paper/2206.03780/) (2022)
- [WaTZ: A Trusted WebAssembly Runtime Environment with Remote Attestation for TrustZone](https://mlsanity.com/paper/2206.08722/) (2022)
- [A Compositional Approach to Creating Architecture Frameworks with an Application to Distributed AI Systems](https://mlsanity.com/paper/2212.13570/) (2022)
- [An investigation of challenges encountered when specifying training data and runtime monitors for safety critical ML applications](https://mlsanity.com/paper/2301.13476/) (2023)
- [Twine: An Embedded Trusted Runtime for WebAssembly](https://mlsanity.com/paper/2103.15860/) (2021)
- [WebAssembly as a Common Layer for the Cloud-edge Continuum](https://mlsanity.com/paper/2206.12888/) (2022)
- [LEGaTO: Low-Energy, Secure, and Resilient Toolset for Heterogeneous Computing](https://mlsanity.com/paper/1912.01563/) (2019)
- [VEDLIoT: Very Efficient Deep Learning in IoT](https://mlsanity.com/paper/2207.00675/) (2022)

## Built on by (most cited first, 1 of 1)

- [RISC-V and machine learning: a survey](https://mlsanity.com/paper/2609.20677/) (2026)

## 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.