Which method minimizes memory usage but may lead to increased training time?

Choosing gradient checkpointing as the method that minimizes memory usage but may lead to increased training time is accurate because gradient checkpointing strategically reduces the amount of memory needed during training.

In traditional backpropagation, all intermediate activations are stored in memory, which can significantly consume resources, especially with large models or long sequences. Gradient checkpointing addresses this by saving only a subset of these activations at designated checkpoints during the forward pass. When backpropagation occurs, the algorithm recomputes the missing activations based on the checkpoints, which enables the reduction of memory footprint.

While this method conserves memory effectively, the trade-off comes in the form of increased computation time since recomputing the activations takes longer than simply pulling them from memory, thus leading to an extended training duration. This balancing act between memory efficiency and computational overhead is a key reason why gradient checkpointing is favored in scenarios where model size is constrained by available memory.

Other choices do not pertain specifically to the memory-computation trade-off in the same way. For instance, synchronous and asynchronous updates relate to how gradients are communicated and updated across multiple workers but do not directly address memory management. Meanwhile, cross-entropy loss is a loss function used to evaluate model performance and