Contextual Sparsity

Contextual sparsity aims to improve the efficiency of large language models (LLMs) by selectively activating only necessary parts of the network based on the input context, thus reducing computational cost without significant accuracy loss. Current research focuses on developing accurate predictors of these sparsity patterns, employing techniques like neural networks and novel activation functions, and designing efficient algorithms and hardware implementations to exploit the sparsity. This approach holds significant promise for deploying LLMs on resource-constrained devices and accelerating inference times, impacting both the scalability of AI applications and the accessibility of advanced language models.