Neuromemristive Multi-Layer Random Projection Network with On-Device Learning

Abstract

This paper proposes a neuromemristive multi-layer neural network with on-device learning. The proposed system is studied within the context of a feedforward multi-layer random projection network, where the core learning is modeled by a stochastic gradient descent simplified for memristor crossbar integration. Two random projection network topologies are explored for binomial and multinomial datasets. A detailed study on the resiliency of the networks in the presence of device failure is performed. The topology with softmax output layer exhibits stability and better resiliency in performance after experiencing a device failure. It is shown that this topology can regain full performance after experiencing 30% stuck-at-faults, with 2x increase in the hidden layer neurons.