Advances in artificial intelligence need to become more resource-aware and sustainable. This requires clear assessment and reporting of energy efficiency trade-offs, like sacrificing fast running time for higher predictive performance. While first methods for investigating efficiency have been proposed, we still lack comprehensive results for popular methods and data sets. In this work, we attempt to fill this information gap by providing empiric insights for popular AI benchmarks, with a total of 100 experiments. Our findings are evidence of how different data sets all have their own efficiency landscape, and show that methods can be more or less likely to act efficiently.

翻译：人工智能的进步需要更加注重资源意识和可持续性。这要求对能源效率的权衡进行清晰评估和报告，例如牺牲较快的运行时间以获得更高的预测性能。尽管已有初步方法用于研究效率问题，但针对流行方法和数据集仍缺乏全面的结果。本研究通过开展总计100项实验，为流行AI基准测试提供实证见解，填补了这一信息空白。我们的发现揭示了不同数据集各自具有独特的效率格局，并表明不同方法在运行效率方面可能存在差异。