Adaptive optimization methods for deep learning adjust the inference task to the current circumstances at runtime to improve the resource footprint while maintaining the model’s performance. These methods are essential for the widespread adoption of deep learning, as they offer a way to reduce the resource footprint of the inference task while also having access to additional information about the current environment. This survey covers the state-of-the-art at-runtime optimization methods, provides guidance for readers to choose the best method for their specific use-case, and also highlights current research gaps in this field.

中文翻译：

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在运行时调整神经网络：深度学习运行时优化的当前趋势

深度学习的自适应优化方法会在运行时根据当前情况调整推理任务，以改善资源占用，同时保持模型的性能。这些方法对于深度学习的广泛采用至关重要，因为它们提供了一种减少推理任务资源占用的方法，同时还可以访问有关当前环境的附加信息。这项调查涵盖了最先进的运行时优化方法，为读者选择适合其特定用例的最佳方法提供了指导，并强调了该领域当前的研究差距。