  The growth in the complexity of Convolutional Neural Networks (CNNs) is
increasing interest in partitioning a network across multiple accelerators
during training and pipelining the backpropagation computations over the
accelerators. Existing approaches avoid or limit the use of stale weights
through techniques such as micro-batching or weight stashing. These techniques
either underutilize of accelerators or increase memory footprint. We explore
the impact of stale weights on the statistical efficiency and performance in a
pipelined backpropagation scheme that maximizes accelerator utilization and
keeps memory overhead modest. We use 4 CNNs (LeNet-5, AlexNet, VGG and ResNet)
and show that when pipelining is limited to early layers in a network, training
with stale weights converges and results in models with comparable inference
accuracies to those resulting from non-pipelined training on MNIST and CIFAR-10
datasets; a drop in accuracy of 0.4%, 4%, 0.83% and 1.45% for the 4 networks,
respectively. However, when pipelining is deeper in the network, inference
accuracies drop significantly. We propose combining pipelined and non-pipelined
training in a hybrid scheme to address this drop. We demonstrate the
implementation and performance of our pipelined backpropagation in PyTorch on 2
GPUs using ResNet, achieving speedups of up to 1.8X over a 1-GPU baseline, with
a small drop in inference accuracy.
