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 resulting from 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.

翻译：卷积神经网络（CNN）是当前应用最广泛的深度神经网络（DNN）架构之一，并在许多问题上达到了最先进的性能。最初应用于计算机视觉任务，CNN不仅能处理图像，还能有效处理具有空间关系的任何数据，并已被应用于不同领域。然而，近期研究指出了DNN中的数值稳定性挑战，这也与其已知的对噪声注入的敏感性相关。这些挑战可能危及模型的性能和可靠性。本文研究了预测蛋白质功能的CNN模型DeepGOPlus。DeepGOPlus已取得最先进性能，能够成功利用并注释蛋白质组学中涌现的大量蛋白质序列。我们通过量化底层浮点数据扰动产生的数值不确定性，确定了模型推理阶段的数值稳定性。此外，我们探索了在DeepGOPlus推理中使用低精度浮点格式以降低内存消耗和延迟的机会。这通过基于蒙特卡洛算术（一种实验性量化浮点运算误差的技术）和VPREC（一种可自定义浮点精度格式模拟结果的工具）对DeepGOPlus执行过程进行检测来实现。重点放在推理阶段，因为这是DeepGOPlus模型的主要交付成果，可广泛应用于不同环境。综上，我们的结果表明，尽管DeepGOPlus CNN在数值上非常稳定，但只能选择性地使用较低精度的浮点格式实现。我们得出结论：预训练DeepGOPlus模型获得的预测在数值上非常可靠，且能高效利用现有浮点格式。