Multi-GPU Training of ConvNets

This work addresses the challenge of scaling deep learning training for researchers and practitioners, but it appears incremental as it focuses on evaluating existing parallelization methods.

The paper tackles the problem of parallelizing convolutional neural network training across multiple GPUs, evaluating different approaches to improve computational efficiency.