Dense Layer

Dense layers, the computationally intensive core of many large neural networks, are a major focus of research aimed at improving efficiency and scalability. Current efforts explore structured matrix alternatives to dense matrices, such as Block Tensor-Train (BTT) and Monarch matrices, and investigate architectures like Mixture-of-Experts (MoE) to distribute computation more effectively. These advancements aim to reduce the computational burden of training and deploying large models, impacting fields like computer vision and natural language processing by enabling the development of more powerful and resource-efficient AI systems.