The single recent focus on deep learning accuracy ignores economic, and environmental cost. Progress towards Green AI is hindered by lack of universal metrics that equally reward accuracy and cost and can help to improve all deep learning algorithms and platforms. We define recognition and training efficiency as new universal metrics to assess deep learning sustainability and compare them to similar, less universal metrics. They are based on energy consumption measurements, on deep learning inference, on recognition gradients, and on number of classes and thus universally balance accuracy, complexity and energy consumption. Well-designed edge accelerators improve recognition and training efficiencies compared to cloud CPUs and GPUs due to reduced communication overhead. Cradle to grave sustainability of edge intelligence models and platforms is assessed with novel deep learning lifecycle efficiency and life cycle recognition efficiency metrics that include the number of times models are used. Artificial and natural intelligence efficiencies are compared leading to insights on deep learning scalability.

最近对深度学习准确性的单一关注忽略了经济和环境成本。由于缺乏同等奖励准确性和成本并有助于改进所有深度学习算法和平台的通用指标，绿色人工智能的进展受到阻碍。我们将识别和训练效率定义为评估深度学习可持续性的新通用指标，并将它们与类似的、不太通用的指标进行比较。它们基于能耗测量、深度学习推理、识别梯度和类别数量，因此普遍平衡了准确性、复杂性和能耗。与云 CPU 和 GPU 相比，由于减少了通信开销，精心设计的边缘加速器可提高识别和训练效率。边缘智能模型和平台从摇篮到严重的可持续性通过新颖的深度学习生命周期效率和生命周期识别效率指标进行评估，这些指标包括使用模型的次数。比较人工智能和自然智能的效率，从而得出对深度学习可扩展性的见解。