The Evolution of MoE: A Review from Basics to Breakthroughs

Rahul Raja, Arpita Vats, Vinija Jain, Aman Chadha

Abstract: This survey delves into the Mixture of Experts (MoE) architecture, a robust and versatile approach designed to enhance the performance and efficiency of deep learning models by leveraging multiple specialized expert models to address complex tasks. We provide a comprehensive analysis of MoE’s fundamental principles, focusing on its core components. Firstly, we examine the dynamic routing mechanisms that intelligently assign input data to the most suitable experts, ensuring optimal utilization of each expert’s specialized capabilities. Secondly, we explore expert specialization strategies, detailing methods for developing and training experts to effectively handle a diverse range of problems, thereby distinguishing MoE from traditional architectures. Additionally, the survey addresses load balancing techniques essential for the efficient distribution of computational resources among experts, which is critical for maintaining high model performance while managing hardware constraints. We also investigate various expert model architectures, from simple neural networks to more intricate, task-specific designs, and their interactions within the MoE framework. A significant contribution of this survey is the presentation of a comprehensive taxonomy that categorizes the key methodologies and compo- nents of MoE, providing a structured overview that facilitates researchers in understanding, comparing, and advancing existing MoE architectures. By organizing the diverse approaches to expert specialization, routing mechanisms, and load balancing, this taxonomy serves as a valuable tool for further innovation and development in the field of MoE-based deep learning models.