Frugal and Lightweight AI for Edge Computing

Scope: The exponential growth of IoT devices and 6G-enabled services requires AI solutions that are efficient, lightweight, and frugal. Conventional AI models are often too resource-hungry for edge devices with limited computation, storage, and energy capacity. This symposium will explore design strategies, architectures, and applications of lightweight AI tailored for resource-constrained environments, including IoT, drones, vehicular systems, and smart cities.

Topics of Interest

• Knowledge distillation, pruning, and quantization for lightweight models
• Efficient deep learning architectures for edge devices
• Frugal AI strategies balancing accuracy, latency, and energy
• Edge intelligence in 6G, IoT, drones, and vehicular systems
• Energy-efficient ML for sustainability
• Deployment frameworks and toolchains for edge AI
• Case studies: smart grids, autonomous systems, and digital twins

Keywords: frugal AI, lightweight ML, edge intelligence, knowledge distillation, IoT, 6G, efficiency.