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Runtime Deep Model Multiplexing for Reduced Latency and Energy Consumption Inference
arXiv - CS - Machine Learning Pub Date : 2020-01-14 , DOI: arxiv-2001.05870
Amir Erfan Eshratifar and Massoud Pedram

We propose a learning algorithm to design a light-weight neural multiplexer that given the input and computational resource requirements, calls the model that will consume the minimum compute resources for a successful inference. Mobile devices can use the proposed algorithm to offload the hard inputs to the cloud while inferring the easy ones locally. Besides, in the large scale cloud-based intelligent applications, instead of replicating the most-accurate model, a range of small and large models can be multiplexed from depending on the input's complexity which will save the cloud's computational resources. The input complexity or hardness is determined by the number of models that can predict the correct label. For example, if no model can predict the label correctly, then the input is considered as the hardest. The proposed algorithm allows the mobile device to detect the inputs that can be processed locally and the ones that require a larger model and should be sent a cloud server. Therefore, the mobile user benefits from not only the local processing but also from an accurate model hosted on a cloud server. Our experimental results show that the proposed algorithm improves mobile's model accuracy by 8.52% which is because of those inputs that are properly selected and offloaded to the cloud server. In addition, it saves the cloud providers' compute resources by a factor of 2.85x as small models are chosen for easier inputs.

中文翻译:

用于减少延迟和能耗推断的运行时深度模型多路复用

我们提出了一种学习算法来设计一个轻量级神经多路复用器,该算法在给定输入和计算资源要求的情况下,调用将消耗最少计算资源的模型以进行成功的推理。移动设备可以使用所提出的算法将硬输入卸载到云端,同时在本地推断简单的输入。此外,在大规模的基于云的智能应用中,不是复制最准确的模型,而是可以根据输入的复杂度复用一系列大小模型,这将节省云的计算资源。输入的复杂性或硬度由可以预测正确标签的模型数量决定。例如,如果没有模型可以正确预测标签,则输入被认为是最难的。所提出的算法允许移动设备检测可以在本地处理的输入以及需要更大模型并应发送到云服务器的输入。因此,移动用户不仅受益于本地处理,还受益于托管在云服务器上的准确模型。我们的实验结果表明,所提出的算法将移动模型的准确度提高了 8.52%,这是因为这些输入被正确选择并卸载到云服务器。此外,由于选择了较小的模型以便于输入,因此它将云提供商的计算资源节省了 2.85 倍。移动用户不仅受益于本地处理,还受益于托管在云服务器上的准确模型。我们的实验结果表明,所提出的算法将移动模型的准确度提高了 8.52%,这是因为这些输入被正确选择并卸载到云服务器。此外,由于选择了较小的模型以便于输入,因此它将云提供商的计算资源节省了 2.85 倍。移动用户不仅受益于本地处理,还受益于托管在云服务器上的准确模型。我们的实验结果表明,所提出的算法将移动模型的准确度提高了 8.52%,这是因为这些输入被正确选择并卸载到云服务器。此外,由于选择了较小的模型以便于输入,因此它将云提供商的计算资源节省了 2.85 倍。
更新日期:2020-09-18
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