With the research advancement of Artificial Intelligence in the last years, there are new opportunities to mitigate real-world problems and advance technologically. Image recognition models in particular, are assigned with perception tasks to mitigate complex real-world challenges and lead to new solutions. Furthermore, the computational complexity and demand for resources of such models has also increased. To mitigate this, model optimization and hardware acceleration has come into play, but effectively integrating such concepts is a challenging and error-prone process. In order to allow developers and researchers to explore the robustness of deep learning image recognition models deployed on different hardware acceleration devices, we propose MutateNN, a tool that provides mutation testing and analysis capabilities for that purpose. To showcase its capabilities, we utilized 21 mutations for 7 widely-known pre-trained deep neural network models. We deployed our mutants on 4 different devices of varying computational capabilities and observed discrepancies in mutants related to conditional operations, as well as some unstable behaviour with those related to arithmetic types.

翻译：随着近年来人工智能研究的进步，出现了缓解现实世界问题并推动技术发展的新机遇。特别是图像识别模型，被赋予感知任务以应对复杂的现实挑战并催生新的解决方案。此外，此类模型的计算复杂度和资源需求也在不断增加。为解决这一问题，模型优化与硬件加速技术应运而生，但有效整合这些概念的过程既具挑战性又易出错。为了让开发者和研究人员能够探索部署在不同硬件加速设备上的深度学习图像识别模型的鲁棒性，我们提出了MutateNN，一个为此提供变异测试和分析能力的工具。为展示其能力，我们对7个广泛使用的预训练深度神经网络模型应用了21种变异，并将变异体部署在4种计算能力不同的设备上。我们观察到与条件操作相关的变异体存在差异，以及与算术类型相关的某些不稳定行为。