  In our previous work we have shown that resistive cross point devices, so
called Resistive Processing Unit (RPU) devices, can provide significant power
and speed benefits when training deep fully connected networks as well as
convolutional neural networks. In this work, we further extend the RPU concept
for training recurrent neural networks (RNNs) namely LSTMs. We show that the
mapping of recurrent layers is very similar to the mapping of fully connected
layers and therefore the RPU concept can potentially provide large acceleration
factors for RNNs as well. In addition, we study the effect of various device
imperfections and system parameters on training performance. Symmetry of
updates becomes even more crucial for RNNs; already a few percent asymmetry
results in an increase in the test error compared to the ideal case trained
with floating point numbers. Furthermore, the input signal resolution to device
arrays needs to be at least 7 bits for successful training. However, we show
that a stochastic rounding scheme can reduce the input signal resolution back
to 5 bits. Further, we find that RPU device variations and hardware noise are
enough to mitigate overfitting, so that there is less need for using dropout.
We note that the models trained here are roughly 1500 times larger than the
fully connected network trained on MNIST dataset in terms of the total number
of multiplication and summation operations performed per epoch. Thus, here we
attempt to study the validity of the RPU approach for large scale networks.
