  Deep Learning and its applications have gained tremendous interest recently
in both academia and industry. Restricted Boltzmann Machines (RBMs) offer a key
methodology to implement deep learning paradigms. This paper presents a novel
approach for realizing hybrid CMOS-OxRAM based deep generative models (DGM). In
our proposed hybrid DGM architectures, HfOx based (filamentary-type switching)
OxRAM devices are extensively used for realizing multiple computational and
non-computational functions such as: (i) Synapses (weights), (ii) internal
neuron-state storage, (iii) stochastic neuron activation and (iv) programmable
signal normalization. To validate the proposed scheme we have simulated two
different architectures: (i) Deep Belief Network (DBN) and (ii) Stacked
Denoising Autoencoder for classification and reconstruction of hand-written
digits from a reduced MNIST dataset of 6000 images. Contrastive-divergence (CD)
specially optimized for OxRAM devices was used to drive the synaptic weight
update mechanism of each layer in the network. Overall learning rule was based
on greedy-layer wise learning with no back propagation which allows the network
to be trained to a good pre-training stage. Performance of the simulated hybrid
CMOS-RRAM DGM model matches closely with software based model for a 2-layers
deep network. Top-3 test accuracy achieved by the DBN was 95.5%. MSE of the SDA
network was 0.003, lower than software based approach. Endurance analysis of
the simulated architectures show that for 200 epochs of training (single RBM
layer), maximum switching events/per OxRAM device was ~ 7000 cycles.
