Supervised learning in spiking neural networks with MLC PCM synapses
We demonstrate for the first time, the feasibility of supervised learning in third generation Spiking Neural Networks (SNNs) using multi-level cell (MLC) phase change memory (PCM) synapses . We highlight two key novel contributions: (i) As opposed to second generation neural networks that are used in machine learning algorithms , or spike timing dependent plasticity based unsupervised learning in SNNs , we use a spike-triggered supervised learning algorithm (NormAD ) for the weight updates. (ii) SNN learning capability is demonstrated using a comprehensive phenomenological model of MLC PCM that accurately captures the statistics of programming inter-cell and intra-cell variability. This work is a harbinger to efficient supervised SNN learning systems.