  Convolutional neural networks (CNNs) are currently among the most widely-used
deep neural network (DNN) architectures available and achieve state-of-the-art
performance for many problems. Originally applied to computer vision tasks,
CNNs work well with any data with a spatial relationship, besides images, and
have been applied to different fields. However, recent works have highlighted
numerical stability challenges in DNNs, which also relates to their known
sensitivity to noise injection. These challenges can jeopardise their
performance and reliability. This paper investigates DeepGOPlus, a CNN that
predicts protein function. DeepGOPlus has achieved state-of-the-art performance
and can successfully take advantage and annotate the abounding protein
sequences emerging in proteomics.We determine the numerical stability of the
model's inference stage by quantifying the numerical uncertainty due to
perturbations of the underlying floating-point data. In addition, we explore
the opportunity to use reduced-precision floating point formats for DeepGOPlus
inference to reduce memory consumption and latency. This is achieved by
instrumenting DeepGOPlus' execution using Monte Carlo Arithmetic, a technique
that experimentally quantifies floating point operation errors and VPREC, a
tool that emulates results with customizable floating point precision formats.
Focus is placed on the inference stage as it is the primary deliverable of the
DeepGOPlus model, widely applicable across different environments. All in all,
our results show that although the DeepGOPlus CNN is very stable numerically,
it can only be selectively implemented with lower-precision floating-point
formats. We conclude that predictions obtained from the pre-trained DeepGOPlus
model are very reliable numerically, and use existing floating-point formats
efficiently.

Machine Learning;Quantitative Methods