Slimmable Neural Network

Slimmable neural networks are a class of models designed to dynamically adjust their size and computational complexity based on available resources, aiming to optimize performance while minimizing energy consumption and memory footprint. Current research focuses on developing efficient training algorithms, such as superposition coding and specialized pruning techniques, and applying these networks to resource-constrained environments like mobile and edge devices, autonomous systems, and federated learning settings. This adaptability makes slimmable networks particularly valuable for applications where computational resources are limited or vary significantly, improving efficiency and enabling deployment on a wider range of hardware.