Experimental Results of EDGE-DNNTuner: Evaluating the Effectiveness of the Symbolic Component in Guiding Neural Architecture Search

This dataset collects the experimental results obtained from the evaluation campaign of EDGE-DNNTuner, a neuro-symbolic Neural Architecture Search (NAS) framework designed to enhance the discovery of Pareto-optimal solutions. The study investigates the contribution of the symbolic reasoning component integrated within the tuner, specifically assessing its capability to effectively guide the exploration process in multi-objective optimization scenarios. The experimental campaign was conducted on three widely adopted benchmark datasets: CIFAR-10, CIFAR-100, and TinyImageNet. Across all test cases, EDGE-DNNTuner consistently demonstrated a strong ability to steer the search toward high-quality regions of the solution space. Notably, the symbolic component enabled a random search strategy to outperform Bayesian optimization, under identical conditions in terms of search space and maximum number of iterations. Results highlight that the integration of symbolic reasoning not only improves the quality of the identified Pareto fronts, but also enhances the overall efficiency of the search process, reducing the need for complex optimization strategies. These findings provide evidence of the effectiveness of hybrid neuro-symbolic approaches in advancing the state of the art in NAS. The dataset includes performance metrics, configurations, and evaluation logs necessary to reproduce and further analyze the reported results.本数据集收录了源自EDGE-DNNTuner评估实验的全部结果。EDGE-DNNTuner是一款神经符号化神经架构搜索（Neural Architecture Search，NAS）框架，旨在优化帕累托最优解的发现流程。本研究旨在探究该调优器内置符号推理组件的作用，并具体评估其在多目标优化场景中有效引导搜索过程的能力。 本次实验基于三类广泛使用的基准数据集展开：CIFAR-10、CIFAR-100与TinyImageNet。在所有测试用例中，EDGE-DNNTuner始终展现出优异的搜索引导能力，可将搜索过程导向解空间的高质量区域。值得注意的是，在搜索空间与最大迭代次数完全一致的前提下，该符号推理组件可使随机搜索策略的表现超越贝叶斯优化。 实验结果表明，符号推理的集成不仅提升了所识别帕累托前沿的质量，同时也优化了搜索整体效率，降低了对复杂优化策略的依赖。上述发现证实了混合神经符号化方法在推动神经架构搜索领域技术前沿发展方面的有效性。 本数据集包含复现并进一步分析报告结果所需的性能指标、模型配置与评估日志。