Neuromorphic hybrid RRAM-CMOS RBM architecture

Restricted Boltzmann Machines (RBMs) offer a key methodology to implement Deep Learning paradigms. This paper presents a novel approach for realizing a hybrid RRAM-CMOS RBM architecture. In our proposed hybrid RBM architecture, HfOx based (filamentary-type switching) RRAM devices are extensively used to implement: (i) Synapses (ii) Internal neuron-state storage and (iii) Stochastic neuron activation function. To validate the proposed scheme we simulated our RBM architecture for classification and reconstruction of hand-written digits on a reduced MNIST dataset of 6000 images. Contrastive-divergence (CD) specially optimized for RRAM devices was used to drive the synaptic weight update mechanism. Total required size of the RRAM matrix in the simulated application is of the order of ~ 0.4 Mb. Peak classification accuracy of 92 %, and an average accuracy of ~ 89 % was obtained over 100 training epochs. Average number of RRAM switching events was ~ 14 million/per epoch.