Limited Memory

Limited memory in machine learning focuses on developing efficient algorithms and architectures that can operate effectively with constrained memory resources, primarily addressing the challenges posed by increasingly large models like LLMs and deep neural networks. Current research emphasizes techniques such as memory-aware attention mechanisms, adaptive memory management strategies (e.g., dynamic caching, swapping), and model compression methods to reduce memory footprint without significant performance loss. This research is crucial for deploying advanced AI models on resource-constrained devices (e.g., edge devices, mobile phones) and for making large-scale model training more accessible.