Distributed Deep Learning using Stochastic Gradient Staleness
This addresses training speed issues for deep learning practitioners, but it is incremental as it builds on existing parallelism strategies.
The paper tackles the slow training of deep neural networks by introducing a distributed method combining data parallelism and decoupled backpropagation, which is proven to converge and shows improved efficiency in training on CIFAR-10.
Despite the notable success of deep neural networks (DNNs) in solving complex tasks, the training process still remains considerable challenges. A primary obstacle is the substantial time required for training, particularly as high performing DNNs tend to become increasingly deep (characterized by a larger number of hidden layers) and require extensive training datasets. To address these challenges, this paper introduces a distributed training method that integrates two prominent strategies for accelerating deep learning: data parallelism and fully decoupled parallel backpropagation algorithm. By utilizing multiple computational units operating in parallel, the proposed approach enhances the amount of training data processed in each iteration while mitigating locking issues commonly associated with the backpropagation algorithm. These features collectively contribute to significant improvements in training efficiency. The proposed distributed training method is rigorously proven to converge to critical points under certain conditions. Its effectiveness is further demonstrated through empirical evaluations, wherein an DNN is trained to perform classification tasks on the CIFAR-10 dataset.