Early eXit

Early exit (EE) techniques aim to improve the efficiency of deep neural networks by allowing predictions to be made at intermediate layers, reducing computational cost and latency. Current research focuses on developing adaptive algorithms and training strategies for EE models, often incorporating techniques like knowledge distillation, multi-armed bandits, and prototypical networks within architectures such as BERT and ResNet. This work is significant because it addresses the growing need for faster and more resource-efficient deep learning inference, particularly in resource-constrained environments like edge devices and mobile applications. The resulting improvements in speed and energy efficiency have broad implications across various fields, including image captioning, speech recognition, and natural language processing.