# Multi-modal Sentiment Analysis using Super Characters Method on Low-power CNN Accelerator Device

arXiv: https://arxiv.org/abs/2001.10179 · HTML: https://mlsanity.com/paper/2001.10179/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2020.

## Built on by (most cited first, 3 of 3)

- [GnetSeg: Semantic Segmentation Model Optimized on a 224mW CNN Accelerator Chip at the Speed of 318FPS](https://mlsanity.com/paper/2101.10444/) (2021)
- [GnetDet: Object Detection Optimized on a 224mW CNN Accelerator Chip at the Speed of 106FPS](https://mlsanity.com/paper/2103.15756/) (2021)
- [SSHPool: The Separated Subgraph-based Hierarchical Pooling](https://mlsanity.com/paper/2403.16133/) (2024)

## Abstract (arXiv)

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.