Extreme Edge

"Extreme edge" research focuses on deploying computationally intensive machine learning models and algorithms on resource-constrained edge devices, prioritizing efficiency and real-time performance without sacrificing accuracy. Current efforts concentrate on optimizing existing architectures like transformers and convolutional neural networks through techniques such as quantization, pruning, knowledge distillation, and novel attention mechanisms, alongside developing lightweight alternatives. This field is crucial for enabling AI applications in diverse areas like robotics, healthcare, and environmental monitoring, where immediate processing and limited power are critical constraints.