Exploiting Oxide Based Resistive RAM Variability for Bayesian Neural Network Hardware Design
This work addresses hardware design for real-time machine learning applications where uncertainty modeling is crucial, representing an incremental advance in specialized hardware.
The paper tackles the challenge of implementing Bayesian neural networks in hardware by using the inherent variability of oxide-based RRAMs to perform probabilistic sampling, achieving a design that converts a typical disadvantage into a functional feature.
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.