Work

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

Year: 2026

Type: conference-paper

Abstract: 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.

Source: IEEE International Symposium on Design and Diagnostics of Electronic Circuits & Systems (DDECS)

DOI: 10.1109/ddecs69233.2026.11521015