Linear Layer

Linear layers are fundamental components of neural networks, and research focuses on improving their efficiency and understanding their role in network behavior. Current efforts explore novel architectures like Block Tensor-Train Mixture-of-Experts (BTT-MoE) and Point Cloud Networks (PCN) to reduce computational costs and memory footprint, alongside techniques like low-rank decomposition and quantization to compress model size. These advancements are crucial for deploying large models on resource-constrained devices and improving the training and inference speed of large language models and other deep learning applications.