Abstract
As one of the emerging challenges in Automated Machine Learning, the Hardware-aware Neural Architecture Search (HW-NAS) tasks can be treated as black-box multi-objective optimization problems (MOPs). An important application of HW-NAS is real-time semantic segmentation, which plays a pivotal role in autonomous driving scenarios. The HW-NAS for real-time semantic segmentation inherently needs to balance multiple optimization objectives, including model accuracy, inference speed, and hardware-specific considerations. Despite its importance, benchmarks have yet to be developed to frame such a challenging task as multi-objective optimization. To bridge the gap, we introduce a tailored streamline to transform the task of HW-NAS for real-time semantic segmentation into standard MOPs. Building upon the streamline, we present a benchmark test suite, CitySeg/MOP, comprising fifteen MOPs derived from the Cityscapes dataset. The CitySeg/MOP test suite is integrated into the EvoXBench platform to provide seamless interfaces with various programming languages (e.g., Python and MATLAB) for instant fitness evaluations. We comprehensively assessed the CitySeg/MOP test suite on various multi-objective evolutionary algorithms, showcasing its versatility and practicality. Source codes are available at this https URL.
Abstract (translated)
作为自动机器学习领域新兴挑战之一，硬件感知的神经架构搜索（HW-NAS）任务可以被视为多目标优化问题（MOPs）。HW-NAS在实时语义分割（Real-time Semantic Segmentation，RSS）中的应用至关重要。为实现实时语义分割，HW-NAS在实时语义分割本身就需要在多个优化目标之间取得平衡，包括模型准确性、推理速度和硬件特定考虑。尽管HW-NAS在实时语义分割中具有重要性，但迄今为止还没有为这样的具有挑战性的任务开发基准。为了填补这一空白，我们引入了一个定制的流线来将HW-NAS在实时语义分割中的任务转化为标准的MOP。在此基础上，我们提出了一个基准测试套件，CitySeg/MOP，包含来自Cityscapes数据集的15个MOP。CitySeg/MOP测试套件已集成到EvoXBench平台中，为各种编程语言（例如Python和MATLAB）提供了一个无缝的界面来进行即时的健身评估。我们对CitySeg/MOP测试套件进行了全面评估，展示了其多才性和实用性。源代码可以从此链接获取：https://url.cn/xyz6uJ4
URL
https://arxiv.org/abs/2404.16266
