Spontaneous sparse learning for PCM-based memristor neural networks

Neural networks trained by backpropagation have achieved tremendous successes on numerous intelligent tasks. However, naive gradient-based training and updating methods on memristors impede applications due to intrinsic material properties. Here, we built a 39nm 1Gb phase change memory (PCM) memristor array and quantified the unique resistance drift effect. On this basis, spontaneous sparse learning (SSL) scheme that leverages the resistance drift to improve PCM-based memristor network training is developed. During training, SSL regards the drift effect as spontaneous consistency-based distillation process that reinforces the array weights at the high-resistance state continuously unless the gradient-based method switches them to low resistance. Experiments show that the SSL not only helps the convergence of network with better performance and sparsity controllability without additional computation in handwritten digit classification. This work promotes the learning algorithms with the intrinsic properties of memristor devices, opening a new direction for development of neuromorphic computing chips.

Automated summary

The research team constructed a 39nm 1Gb phase-change memory memristor array and developed a spontaneous sparse learning scheme to improve PCM-based memristor network training. Experimental results show that this method helps enhance the performance and sparsity controllability of the network without requiring additional computation.