Key Value Memory

Key-value memory (KVM) is a rapidly developing area focusing on efficient data storage and retrieval within neural networks, particularly transformers. Current research emphasizes optimizing KVM architectures for reduced memory footprint and improved inference speed, exploring techniques like L2 norm-based compression and dynamic memory allocation, often within the context of large language models. These advancements are crucial for deploying large models on resource-constrained devices and improving the efficiency of various NLP and computer vision tasks, addressing the limitations of traditional memory-intensive approaches.