DiverGet：一种基于搜索的深度神经网络Quantization评估软件测试方法
DiverGet: A Search-Based Software Testing Approach for Deep Neural Network Quantization Assessment
摘要：
当在嵌入式系统或手机上部署经过训练的深度神经网络（DNN）模型时，量化是应用最广泛的DNN压缩策略之一。这是由于其简单性和对广泛应用和环境的适应性，而不是通常仅为某些特定硬件（例如，Google Coral Edge TPU）。随着对量化的需求不断增长，确保该策略的可靠性正成为一个关键挑战。传统的测试方法收集越来越多的真实数据以进行更好的评估，但由于输入空间很大以及原始DNN与量化DNN之间的高度相似性，因此通常不实用。因此，先进的评估战略变得至关重要。在本文中，我们提出了DiverGet，基于搜索的量化评估的测试框架。DiverGet定义了一个变形关系的空间，模拟输入上自然发生的扭曲。然后，它最佳地探索这些关系，以揭示不同算法精度的DNN之间的分歧。我们评估了DiverGet在应用于高光谱遥感图像的最先进DNN上的性能。我们选择了遥感DNN，因为它们越来越多地部署在边缘（例如，高升力无人机）在气候变化研究和天文学等关键领域。我们的研究结果表明，DiverGet成功地挑战了现有量化技术对自然发生的移位数据的鲁棒性，并优于其最新的并发DiffChaser，成功率（平均）高出四倍。
英文摘要：
Quantization is one of the most applied Deep Neural Network (DNN) compression strategies, when deploying a trained DNN model on an embedded system or a cell phone. This is owing to its simplicity and adaptability to a wide range of applications and circumstances, as opposed to specific Artificial Intelligence (AI) accelerators and compilers that are often designed only for certain specific hardware (e.g., Google Coral Edge TPU). With the growing demand for quantization, ensuring the reliability of this strategy is becoming a critical challenge. Traditional testing methods, which gather more and more genuine data for better assessment, are often not practical because of the large size of the input space and the high similarity between the original DNN and its quantized counterpart. As a result, advanced assessment strategies have become of paramount importance. In this paper, we present DiverGet, a search-based testing framework for quantization assessment. DiverGet defines a space of metamorphic relations that simulate naturally-occurring distortions on the inputs. Then, it optimally explores these relations to reveal the disagreements among DNNs of different arithmetic precision. We evaluate the performance of DiverGet on state-of-the-art DNNs applied to hyperspectral remote sensing images. We chose the remote sensing DNNs as they're being increasingly deployed at the edge (e.g., high-lift drones) in critical domains like climate change research and astronomy. Our results show that DiverGet successfully challenges the robustness of established quantization techniques against naturally-occurring shifted data, and outperforms its most recent concurrent, DiffChaser, with a success rate that is (on average) four times higher.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
