Abstract
The growing demand for deployment of Artificial Intelligence (AI) on resource-constrained edge devices has motivated extensive research on the design of efficient edge-compatible AI hardware accelerators. One of the most promising solutions are the self-adaptive AI accelerators, capable of optimizing in real time their performance and energy consumption according to application requirements. This work introduces the EU-funded project Twinning for Excellence in Adaptive Edge Artificial Intelligence (AIDA4Edge), aimed to advance the state-of-the-art in the design of adaptive neural network accelerators for edge applications. The main goal is to develop a novel hybrid self-adaptive neural network architecture combining spiking and artificial neural networks, and supporting runtime adaptation of network functionality, precision and reliability. Furthermore, we aim to enhance the neural network training by incorporating hardware and quantization constraints in an automated tuning engine.
| Original language | English |
|---|---|
| Title of host publication | 28th Euromicro Conference Series on Digital System Design (DSD) 2025 |
| Publication status | Accepted/In press - 26 Jun 2025 |
Keywords
- Artificial neural networks
- spiking neural networks
- hybrid neural networks
- adaptive neural networks
- neural network accelerators
- hyperparameter optimization
- quantization
- reliability of neural networks