Dynamic Neural Network

Dynamic neural networks (DNNs) are a class of neural networks designed to adapt their computational resources to the complexity of the input, aiming for improved efficiency and reduced energy consumption without sacrificing accuracy. Current research focuses on developing novel architectures like early-exiting networks, dynamic feature aggregation modules, and hypernetworks, often incorporating techniques such as finite state machines for efficient resource management and adaptive routing for optimal inference paths. This field is significant because it addresses the limitations of static DNNs in resource-constrained environments, offering potential for improved performance in applications ranging from mobile computing and robotics to real-time object detection and natural language processing.