Abstract

This paper provides a comprehensive overview of MoE, covering its fundamental principles, architectural variations, advantages, limitations, and potential future directions. We delve into the core concepts of MoE, including the gating network, expert networks, and routing mechanisms, and discuss how these components work together to achieve specialization and efficiency. We also examine the application of MoE in models like GPT-4 and Mixtral, highlighting their impact on the field of AI. We cover theoretical foundations, hardware and software innovations, real-world deployments, and the evolving landscape of MoE research. This paper furthur provides a comprehensive survey of MoE architectures, tracing their evolution from early neural network implementations to modern large-scale applications in language models, time series forecasting, and tabular data analysis. Next, the paper examines how machine learning is applied to natural language processing, computer vision, finance and healthcare. We examine major problems, including routing imbalance, memory fragmentation and instability during training, by checking newly proposed answers found in research papers. After all, we summarize possible future areas of study and discuss how MoE models could transform the world of artificial intelligence moving forward. All the results in this paper are taken from the cited work.