Convolutional neural networks (CNNs) have become the compelling solution in machine learning applications as they surpass human-level accuracy in a certain set of tasks. Despite the success of CNNs, they classify images based on the identification of specific features, ignoring the spatial relationships between different features due to the pooling layer. The capsule network (CapsNet) architecture proposed by Google Brain's team is an attempt to address this drawback by grouping several neurons into a single capsule and learning the spatial correlations between different input features. Thus, the CapsNet identifies not only the presence of a feature but also its relationship with other features. However, the success of the CapsNet comes at the cost of underutilization of resources when it is run on a modern GPU equipped with tensor cores (TCs). Due to the structure of capsules in the CapsNet, quite often, functional units in a TC are underutilized which prolong the execution of capsule layers and increase energy consumption. In this work, we propose an architecture to eliminate ineffectual operations and improve energy-efficiency of GPUs. Experimental measurements over a set of state-of-the-art datasets show that the proposed approach improves energy-efficiency by 15% while maintaining the accuracy of CapsNets.

中文翻译：

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提高现代 GPU 上胶囊网络的能源效率

卷积神经网络 (CNN) 已成为机器学习应用中引人注目的解决方案，因为它们在某些任务组中的准确性超过了人类水平。尽管 CNN 取得了成功，但它们基于特定特征的识别对图像进行分类，由于池化层而忽略了不同特征之间的空间关系。 Google Brain 团队提出的胶囊网络（CapsNet）架构试图通过将多个神经元分组到单个胶囊中并学习不同输入特征之间的空间相关性来解决这一缺陷。因此，CapsNet 不仅识别某个特征的存在，还识别它与其他特征的关系。然而，CapsNet 的成功是以在配备张量核心 (TC) 的现代 GPU 上运行时资源利用不足为代价的。由于 CapsNet 中胶囊的结构，TC 中的功能单元常常未得到充分利用，这延长了胶囊层的执行时间并增加了能耗。在这项工作中，我们提出了一种架构来消除无效操作并提高 GPU 的能源效率。对一组最先进数据集的实验测量表明，所提出的方法将能源效率提高了 15%，同时保持了 CapsNet 的准确性。