Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and FPGAs for fast, timely processing. Failure in real-time image recognition tasks can occur due to incorrect mapping on hardware accelerators, which may lead to timing uncertainty and incorrect behavior. Owing to the increased use of image recognition tasks in safety-critical applications like autonomous driving and medical imaging, it is imperative to assess their robustness to changes in the computational environment as parameters like deep learning frameworks, compiler optimizations for code generation, and hardware devices are not regulated with varying impact on model performance and correctness. In this paper we conduct robustness analysis of four popular image recognition models (MobileNetV2, ResNet101V2, DenseNet121 and InceptionV3) with the ImageNet dataset, assessing the impact of the following parameters in the model's computational environment: (1) deep learning frameworks; (2) compiler optimizations; and (3) hardware devices. We report sensitivity of model performance in terms of output label and inference time for changes in each of these environment parameters. We find that output label predictions for all four models are sensitive to choice of deep learning framework (by up to 57%) and insensitive to other parameters. On the other hand, model inference time was affected by all environment parameters with changes in hardware device having the most effect. The extent of effect was not uniform across models.
翻译:图像识别任务通常使用深层学习,需要巨大的处理能力,从而依靠GPUs和FPGAs等硬件加速器进行快速、及时处理。实时图像识别任务之所以失败,是因为硬件加速器的不正确测绘可能导致时间不确定性和不正确行为。由于在安全关键应用中更多地使用图像识别任务,例如自主驱动和医学成像,因此有必要评估这些任务对计算环境变化的稳健性,将其作为深层学习框架、代码生成的编译优化和硬件装置等参数,对模型性能和正确性的影响不同。在本文件中,我们对四种受欢迎的图像识别模型模型(MobileNetV2、ResNet101V2、DenseNet121和InceptionV3)进行稳健分析,这可能会导致时间的不确定性和不正确性。由于在图像网络数据集中更多地使用图像识别任务,评估模型计算环境中以下参数的影响:(1) 深层学习框架;(2) 编译优化;(3) 硬件装置。我们报告模型性表现的敏感性,对模型的性能产生不同程度的影响,每个环境参数的变化时间。我们发现,在五十七项结构中,最敏感的参数预测是最敏感的数值框架中,对五十七项的数值的影响。