Deep neural networks are widely used in many artificial intelligence applications. They have demonstrated state-of-the-art accuracy on many artificial intelligence tasks. For this high accuracy to occur, deep neural networks require the right parameter values. This is achieved by a process known as training . The training of large amounts of data via many iterations comes at a high cost in regard to computation time and energy. Optimal resource allocation would therefore reduce the training time. TensorFlow, a computational graph library developed by Google, alleviates the development of neural network models and provides the means to train these networks. In this article, we propose Adaptive HTF-MPR to carry out the resource allocation, or mapping, on TensorFlow. Adaptive HTF-MPR searches for the best mapping in a hybrid approach. We applied the proposed methodology on two well-known image classifiers: VGG-16 and AlexNet. We also performed a full analysis of the solution space of MNIST Softmax. Our results demonstrate that Adaptive HTF-MPR outperforms the default homogeneous TensorFlow mapping. In addition to the speed up, Adaptive HTF-MPR can react to changes in the state of the system and adjust to an improved mapping.

中文翻译：

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自适应 HTF-MPR

深度神经网络广泛用于许多人工智能应用。他们在许多人工智能任务上展示了最先进的准确性。为了实现这种高精度，深度神经网络需要正确的参数值。这是通过称为训练. 通过多次迭代训练大量数据在计算时间和能量方面的成本很高。因此，最佳资源分配将减少培训时间。TensorFlow 是谷歌开发的计算图库，缓解了神经网络模型的开发，并提供了训练这些网络的方法。在本文中，我们提出自适应 HTF-MPR在 TensorFlow 上进行资源分配或映射。自适应 HTF-MPR 在混合方法中搜索最佳映射。我们将所提出的方法应用于两个著名的图像分类器：VGG-16 和 AlexNet。我们还对 MNIST Softmax 的解空间进行了全面分析。我们的结果表明，自适应 HTF-MPR 优于默认的同质 TensorFlow 映射。除了加速之外，自适应 HTF-MPR 还可以对系统状态的变化做出反应并适应改进的映射。