Deploying complex deep learning models on edge devices is challenging because
they have substantial compute and memory resource requirements, whereas edge
devices' resource budget is limited. To solve this proble
Layer Adaptive Progressive Pruning (LAPP) is a novel framework that introduces a learnable threshold for each layer and FLOPs constraints for network, gradually compressing the network during initial training and automatically determining appropriate pruning rates for each layer.