论文标题
探索计算参数变化对图像识别系统的效果
Exploring Effects of Computational Parameter Changes to Image Recognition Systems
论文作者
论文摘要
图像识别任务通常使用深度学习并需要巨大的处理能力，从而依靠GPU和FPGA等硬件加速器进行快速，及时的处理。实时图像识别任务中的故障可能是由于硬件加速器上的不正确映射而发生的，这可能导致时间不确定性和不正确的行为。由于在自动驾驶和医学成像（例如自动驾驶和医学成像）中增加了图像识别任务的使用增加，因此必须评估它们对计算环境变化的稳健性，因为参数，诸如深度学习框架，编译器的代码生成优化以及硬件设备的优化以及对模型性能和正确性的影响都不会受到调节。在本文中，我们使用Imagenet数据集对四个流行图像识别模型（Mobilenetv2，Resnet101V2，Densenet121和InctepionV3）进行了鲁棒性分析，评估了以下参数在模型计算环境中的影响：（1）深度学习框架； （2）编译器优化； （3）硬件设备。我们报告了模型性能的敏感性，以输出标签和推理时间的变化时间来改变这些环境参数。我们发现，所有四个模型的输出标签预测都对选择深度学习框架（最多57％）敏感，并且对其他参数不敏感。另一方面，模型推理时间受所有环境参数的影响，硬件设备的变化具有最大的效果。效果程度在模型之间并不统一。
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.
