Spontaneous sparse learning for PCM-based memristor neural networks

Abstract

Designing energy efficient and scalable artificial networks for neuromorphic computing remains a challenge. Here, the authors present 1 Gb phase change memory memristor array with a spontaneous sparse learning scheme able to leverage the resistance drift issue improving the classification accuracy on MNIST handwritten digit dataset.