  Bayesian neural networks (BNNs) are making significant progress in many
research areas where decision-making needs to be accompanied by uncertainty
estimation. Being able to quantify uncertainty while making decisions is
essential for understanding when the model is over-/under-confident, and hence
BNNs are attracting interest in safety-critical applications, such as
autonomous driving, healthcare, and robotics. Nevertheless, BNNs have not been
as widely used in industrial practice, mainly because of their increased memory
and compute costs. In this work, we investigate quantisation of BNNs by
compressing 32-bit floating-point weights and activations to their integer
counterparts, that has already been successful in reducing the compute demand
in standard pointwise neural networks. We study three types of quantised BNNs,
we evaluate them under a wide range of different settings, and we empirically
demonstrate that a uniform quantisation scheme applied to BNNs does not
substantially decrease their quality of uncertainty estimation.
