Memory Efficient

Memory-efficient deep learning focuses on developing and optimizing neural network models and training algorithms to minimize memory consumption, enabling deployment on resource-constrained devices and scaling to larger models. Current research emphasizes techniques like neural architecture search (NAS) to design inherently efficient architectures (e.g., optimized convolutional and transformer networks, memory-efficient Graph Neural Networks), low-rank approximations, mixed-precision training, and novel optimization algorithms (e.g., variants of Adam, Shampoo) that reduce memory overhead during both training and inference. These advancements are crucial for expanding the accessibility and applicability of deep learning across diverse hardware platforms and datasets, particularly in edge computing and large language model training.