From smartphones and wearables to autonomous vehicles, robots, and AR/VR systems, the demand for models that are efficient, private, and adaptive in real-time has never been higher. Yet deploying state-of-the-art AI at the edge remains challenging: researchers and practitioners must navigate heterogeneous hardware, memory and power constraints, compression and distillation trade-offs, as well as privacy, safety, and reliability requirements.

This workshop will bring together researchers, practitioners, and industry leaders to explore the frontiers of Edge AI. Topics will include lightweight model architectures, compiler/toolchain optimizations (e.g., quantization, pruning, sparsity), advances in frameworks such as ExecuTorch and TensorRT, distributed learning across devices, privacy-preserving training, and emerging applications where latency and trust are critical. Beyond technical advances, we will examine the broader implications for democratizing AI—enabling billions of devices to act as intelligent, personalized agents while reducing dependence on the cloud.