Lightweight Neural Networks are machine learning models designed to perform tasks using fewer computational resources than larger neural networks. They achieve this by reducing the number of parameters, simplifying network structures, or using techniques that make calculations more efficient while maintaining useful accuracy. This allows them to process information with less memory, energy, and processing power, making them suitable for devices with limited hardware capabilities.
For example, a smartphone camera can use a lightweight neural network to recognize objects or improve images directly on the device. This reduces reliance on remote servers and can provide faster responses. They are widely used in mobile applications, edge computing, IoT devices, robotics, embedded systems, smart cameras, and other resource-constrained environments.
