Compression Framework

Compression frameworks are being actively developed to address the growing demands of large-scale machine learning models and high-resolution data streams, aiming to reduce computational costs and memory usage without significant performance loss. Current research focuses on techniques like knowledge distillation, model pruning, and the development of novel compression algorithms tailored to specific data types (e.g., video, spike streams, embeddings) and model architectures (e.g., Transformers). These advancements are crucial for deploying complex models on resource-constrained devices and improving the efficiency of various applications, including recommendation systems, video processing, and distributed machine learning.