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.
@article{arxiv.2001.10179,
title = {Multi-modal Sentiment Analysis using Super Characters Method on Low-power CNN Accelerator Device},
author = {Baohua Sun and Lin Yang and Hao Sha and Michael Lin},
journal= {arXiv preprint arXiv:2001.10179},
year = {2020}
}