Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices

Understand

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.