Low Power Computer Vision

Low-power computer vision focuses on developing computer vision systems that operate efficiently on resource-constrained devices, prioritizing both accuracy and minimal energy consumption. Current research emphasizes techniques like spiking neural networks (SNNs) and efficient training methods that reduce computational complexity, often employing novel regularization strategies or runtime adaptation of model parameters to optimize for specific hardware and accuracy needs. This field is crucial for enabling the deployment of computer vision in mobile and embedded applications, such as drone-based disaster response and real-time object tracking, where power limitations are significant constraints.