Version 1
: Received: 7 August 2024 / Approved: 8 August 2024 / Online: 8 August 2024 (08:39:13 CEST)
Version 2
: Received: 21 August 2024 / Approved: 22 August 2024 / Online: 23 August 2024 (03:05:11 CEST)
How to cite:
Vats, A.; Raja, R.; Jain, V.; Chadha, A. The Evolution of Mixture of Experts: A Survey from Basics to Breakthroughs. Preprints2024, 2024080583. https://doi.org/10.20944/preprints202408.0583.v2
Vats, A.; Raja, R.; Jain, V.; Chadha, A. The Evolution of Mixture of Experts: A Survey from Basics to Breakthroughs. Preprints 2024, 2024080583. https://doi.org/10.20944/preprints202408.0583.v2
Vats, A.; Raja, R.; Jain, V.; Chadha, A. The Evolution of Mixture of Experts: A Survey from Basics to Breakthroughs. Preprints2024, 2024080583. https://doi.org/10.20944/preprints202408.0583.v2
APA Style
Vats, A., Raja, R., Jain, V., & Chadha, A. (2024). The Evolution of Mixture of Experts: A Survey from Basics to Breakthroughs. Preprints. https://doi.org/10.20944/preprints202408.0583.v2
Chicago/Turabian Style
Vats, A., Vinija Jain and Aman Chadha. 2024 "The Evolution of Mixture of Experts: A Survey from Basics to Breakthroughs" Preprints. https://doi.org/10.20944/preprints202408.0583.v2
Abstract
The Mixture of Experts (MoE) architecture has evolved as a powerful and versatile approach for improving the performance and efficiency of deep learning models. This survey aims to provide the fundamental principles of MoE in details, a paradigm that harnesses the collective power of multiple specialized "expert" models working in concert to tackle complex tasks.Our exploration is a thorough analysis of the core components that constitute the MoE framework. We begin by dissecting the routing mechanism, a crucial element responsible for dynamically assigning input data to the most appropriate expert models. This routing process is pivotal in ensuring that each expert's specialized knowledge is optimally utilized. The survey places significant emphasis on expert specialization, a key feature that sets MoE apart from traditional architectures. We examine various strategies for developing and training specialized experts, exploring how this specialization enables MoE models to effectively handle diverse and multifaceted problems.Load balancing, another critical aspect of MoE systems, receives thorough attention. We discuss techniques for efficiently distributing computational resources among experts, ensuring optimal model performance while managing hardware constraints. This section provides insights into the delicate balance between model capacity and computational efficiency. Furthermore, we provide an in-depth discussion of the expert models themselves, examining their architectural designs, training methodologies, and how they interact within the larger MoE framework. This includes an analysis of different types of experts, from simple neural networks to more complex, task-specific architectures. The survey also navigates through diverse research avenues within the MoE landscape, highlighting recent advancements and innovative applications across various domains of machine learning. We pay particular attention to the burgeoning use of MoE in two rapidly evolving fields: computer vision and large language model (LLM) scaling. By providing this comprehensive overview, our survey aims to offer researchers and practitioners a deep understanding of MoE's capabilities, current applications, and potential future directions in the ever-evolving landscape of deep learning.The Mixture of Experts (MoE) architecture has evolved as a powerful and versatile approach for improving the performance and efficiency of deep learning models. This survey aims to provide the fundamental principles of MoE in details, a paradigm that harnesses the collective power of multiple specialized "expert" models working in concert to tackle complex tasks.Our exploration is a thorough analysis of the core components that constitute the MoE framework. We begin by dissecting the routing mechanism, a crucial element responsible for dynamically assigning input data to the most appropriate expert models. This routing process is pivotal in ensuring that each expert's specialized knowledge is optimally utilized. The survey places significant emphasis on expert specialization, a key feature that sets MoE apart from traditional architectures. We examine various strategies for developing and training specialized experts, exploring how this specialization enables MoE models to effectively handle diverse and multifaceted problems.Load balancing, another critical aspect of MoE systems, receives thorough attention. We discuss techniques for efficiently distributing computational resources among experts, ensuring optimal model performance while managing hardware constraints. This section provides insights into the delicate balance between model capacity and computational efficiency. Furthermore, we provide an in-depth discussion of the expert models themselves, examining their architectural designs, training methodologies, and how they interact within the larger MoE framework. This includes an analysis of different types of experts, from simple neural networks to more complex, task-specific architectures. The survey also navigates through diverse research avenues within the MoE landscape, highlighting recent advancements and innovative applications across various domains of machine learning. We pay particular attention to the burgeoning use of MoE in two rapidly evolving fields: computer vision and large language model (LLM) scaling. By providing this comprehensive overview, our survey aims to offer researchers and practitioners a deep understanding of MoE's capabilities, current applications, and potential future directions in the ever-evolving landscape of deep learning.
Keywords
Mixture of Experts; LLM; Computer Vision
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.