  New hardware can substantially increase the speed and efficiency of deep
neural network training. To guide the development of future hardware
architectures, it is pertinent to explore the hardware and machine learning
properties of alternative training algorithms. In this work we evaluate the use
of small batch, fine-grained Pipelined Backpropagation, an asynchronous
pipeline parallel training algorithm that has significant hardware advantages.
We introduce two methods, Spike Compensation and Linear Weight Prediction, that
effectively mitigate the downsides caused by the asynchronicity of Pipelined
Backpropagation and outperform existing techniques in our setting. We show that
appropriate normalization and small batch sizes can also aid training. With our
methods, fine-grained Pipelined Backpropagation using a batch size of one can
match the accuracy of SGD for multiple networks trained on CIFAR-10 and
ImageNet. Simple scaling rules allow the use of existing hyperparameters for
traditional training without additional tuning.
