Sparse Activation

Sparse activation, the phenomenon where only a small subset of neurons are active during neural network processing, is a key research area aiming to improve the efficiency and speed of large-scale models like Transformers and Mixture-of-Experts (MoE) architectures. Current research focuses on optimizing training methods for sparse models, developing novel activation functions to enhance sparsity, and exploring the interplay between sparse activation and other efficiency techniques like weight pruning and quantization. This research is significant because it offers the potential for substantial reductions in computational cost and energy consumption for large language models and other deep learning applications, making them more accessible and sustainable.