Multi-modal Sentiment Analysis using Super Characters Method on Low-power CNN Accelerator Device
This work addresses power efficiency for deploying NLP systems in resource-constrained environments, though it is incremental as it adapts an existing method to a new hardware platform.
The paper tackled the problem of high power consumption in NLP systems by implementing the Super Characters method on a low-power CNN accelerator device, achieving a 30% reduction in power usage while maintaining competitive accuracy on the CL-Aff shared task.
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.