  Uncertainty plays a key role in real-time machine learning. As a significant
shift from standard deep networks, which does not consider any uncertainty
formulation during its training or inference, Bayesian deep networks are being
currently investigated where the network is envisaged as an ensemble of
plausible models learnt by the Bayes' formulation in response to uncertainties
in sensory data. Bayesian deep networks consider each synaptic weight as a
sample drawn from a probability distribution with learnt mean and variance.
This paper elaborates on a hardware design that exploits cycle-to-cycle
variability of oxide based Resistive Random Access Memories (RRAMs) as a means
to realize such a probabilistic sampling function, instead of viewing it as a
disadvantage.
