Memory Intensive Backpropagation

Memory-intensive backpropagation, a crucial step in training large neural networks, is a significant bottleneck in deep learning. Current research focuses on reducing this memory burden through techniques like approximate backpropagation, memory sharing strategies, and the exploration of backpropagation-free alternatives such as forward-forward learning. These efforts aim to improve the efficiency of training large models, particularly for fine-tuning pre-trained networks and enabling deployment on resource-constrained devices. The resulting advancements promise to accelerate research and broaden the applicability of deep learning to a wider range of tasks and hardware platforms.