  Decoupled learning is a branch of model parallelism which parallelizes the
training of a network by splitting it depth-wise into multiple modules.
Techniques from decoupled learning usually lead to stale gradient effect
because of their asynchronous implementation, thereby causing performance
degradation. In this paper, we propose an accumulated decoupled learning (ADL)
which incorporates the gradient accumulation technique to mitigate the stale
gradient effect. We give both theoretical and empirical evidences regarding how
the gradient staleness can be reduced. We prove that the proposed method can
converge to critical points, i.e., the gradients converge to 0, in spite of its
asynchronous nature. Empirical validation is provided by training deep
convolutional neural networks to perform classification tasks on CIFAR-10 and
ImageNet datasets. The ADL is shown to outperform several state-of-the-arts in
the classification tasks, and is the fastest among the compared methods.
