Special Session: Optimizing Edge AI - Current Challenges and the Neuromorphic Outlook

The increasing deployment of AI (artificial intelligence) on edge devices presents major challenges due to strict constraints on computation, memory, energy, and latency. Effective Edge AI systems thus require multi-objective optimization that balances accuracy, hardware efficiency, and reliability. The Horizon Twinning project AIDA4Edge tackles these challenges by developing methods for efficient and reliable AI on resource-constrained platforms. This paper presents key approaches explored within the project, including neural network quantization, hardware-aware neural architecture search, dynamic neural networks, and self-adaptive resilient AI architectures. Finally, these strategies are placed within a broader, biologically inspired paradigm, highlighting neuromorphic computing as a natural continuation of Edge AI efforts toward highly efficient and resilient intelligent systems.