论文标题
使用超功能CNN加速器设备上的超级角色方法的多模式情感分析
Multi-modal Sentiment Analysis using Super Characters Method on Low-power CNN Accelerator Device
论文作者
论文摘要
近年来，NLP研究见证了DNN模型的创纪录的准确性提高。但是，功耗是部署NLP系统的实际问题之一。大多数当前最新算法都是在GPU上实现的，该算法不是强力效率，部署成本也很高。另一方面，CNN域特异性加速器（CNN-DSA）已在质量生产中提供低功率和低成本的计算功率。在本文中，我们将在CNN-DSA上实现超级字符方法。此外，我们修改了使用多模式数据的超级字符方法，即Cl-aff共享任务中的文本加上表格数据。
Recent years NLP research has witnessed the record-breaking accuracy improvement by DNN models. However, power consumption is one of the practical concerns for deploying NLP systems. Most of the current state-of-the-art algorithms are implemented on GPUs, which is not power-efficient and the deployment cost is also very high. On the other hand, CNN Domain Specific Accelerator (CNN-DSA) has been in mass production providing low-power and low cost computation power. In this paper, we will implement the Super Characters method on the CNN-DSA. In addition, we modify the Super Characters method to utilize the multi-modal data, i.e. text plus tabular data in the CL-Aff sharedtask.
