Title: Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices,
Abstract: In a previous work we have detailed the requirements to obtain a maximal
performance benefit by implementing fully connected deep neural networks (DNN)
in form of arrays of resistive devices for deep learning. This concept of
Resistive Processing Unit (RPU) devices we extend here towards convolutional
neural networks (CNNs). We show how to map the convolutional layers to RPU
arrays such that the parallelism of the hardware can be fully utilized in all
three cycles of the backpropagation algorithm. We find that the noise and bound
limitations imposed due to analog nature of the computations performed on the
arrays effect the training accuracy of the CNNs. Noise and bound management
techniques are presented that mitigate these problems without introducing any
additional complexity in the analog circuits and can be addressed by the
digital circuits. In addition, we discuss digitally programmable update
management and device variability reduction techniques that can be used
selectively for some of the layers in a CNN. We show that combination of all
those techniques enables a successful application of the RPU concept for
training CNNs. The techniques discussed here are more general and can be
applied beyond CNN architectures and therefore enables applicability of RPU
approach for large class of neural network architectures.