Feature Reuse

Feature reuse in machine learning aims to improve efficiency and reduce computational costs by leveraging previously computed information or learned representations across different tasks or within a single model. Current research focuses on developing techniques for efficient feature reuse in various contexts, including accelerating large language models (LLMs) through prompt sharing, optimizing deep neural networks (DNNs) by reusing features within and across layers, and enhancing the speed of diffusion models via feature map reuse. These advancements have significant implications for improving the scalability and performance of various machine learning applications, ranging from image generation and natural language processing to large-scale data analytics and real-time video processing.