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Numerical stability of DeepGOPlus inference.

Inés Gonzalez Pepe¹, Yohan Chatelain¹, Gregory Kiar²

¹Department of Computer Science and Software Engineering, Concordia University, Montreal, Qc, Canada.

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DeepGOPlus, a convolutional neural network (CNN) for protein function prediction, demonstrates strong numerical stability. While generally reliable, its use with reduced-precision floating-point formats for inference is selective.

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Area of Science:

Computational biology
Bioinformatics
Machine learning

Background:

Convolutional neural networks (CNNs) excel in various domains, including protein function prediction.
Deep neural networks (DNNs) face numerical stability challenges, impacting reliability and noise sensitivity.
DeepGOPlus is a state-of-the-art CNN for annotating protein sequences in proteomics.

Purpose of the Study:

To assess the numerical stability of the DeepGOPlus model during its inference stage.
To explore the use of reduced-precision floating-point formats for DeepGOPlus inference to optimize memory and latency.
To quantify numerical uncertainty arising from floating-point data perturbations.

Main Methods:

Quantified numerical uncertainty in DeepGOPlus inference by perturbing floating-point data.
Utilized Monte Carlo Arithmetic to experimentally measure floating-point operation errors.
Emulated results with customizable floating-point precision using the VPREC tool.

Main Results:

The DeepGOPlus CNN exhibits high numerical stability in its inference stage.
Selective implementation with lower-precision floating-point formats is feasible.
The model efficiently utilizes existing floating-point formats.

Conclusions:

DeepGOPlus predictions are numerically reliable.
Optimizing DeepGOPlus for reduced-precision formats requires careful consideration.
The model demonstrates efficient use of current floating-point standards.