Power-efficient CNN Domain Specific Accelerator (CNN-DSA) chips are currently available for wide use in mobile devices. These chips are mainly used in computer vision applications. However, the recent work of Super Characters method for text classification and sentiment analysis tasks using two-dimensional CNN models has also achieved state-of-the-art results through the method of transfer learning from vision to text. In this paper, we implemented the text classification and sentiment analysis applications on mobile devices using CNN-DSA chips. Compact network representations using one-bit and three-bits precision for coefficients and five-bits for activations are used in the CNN-DSA chip with power consumption less than 300mW. For edge devices under memory and compute constraints, the network is further compressed by approximating the external Fully Connected (FC) layers within the CNN-DSA chip. At the workshop, we have two system demonstrations for NLP tasks. The first demo classifies the input English Wikipedia sentence into one of the 14 classes. The second demo classifies the Chinese online-shopping review into positive or negative. Power-efficient CNN Domain Specific Accelerator (CNN-DSA) chips are currently available for wide use. Sun et al. BID5 ;a) designed a two-dimensional CNN-DSA accelerator which achieved a power consumption of less than 300mW and an ultra power-efficiency of 9.3TOPS/Watt. All the processing is in internal memory instead of external DRAM. Demos on mobile and embedded systems show its applications in real-world implemen-Preliminary work. Under review by the International Conference on Machine Learning (ICML). Do not distribute. Figure 1 . Efficient On-Device Natural Language Processing system demonstration. The CNN-DSA chip is connected to Raspberry Pi through the USB interface. Keyboard sends the typing text input to Raspberry Pi through USB. A monitor is connected to Raspberry Pi through HDMI for display. On the monitor, it shows the introduction for the demo (zoom in to see details). There are two demos. The first demo classifies the input English Wikipedia sentence into one of the 14 ontologies. The second demo classifies the Chinese online-shopping review into positive or negative.tations. The 28nm CNN-DSA accelerator attains a 140fps for 224x224 RGB image inputs at an accuracy comparable to that of the VGG BID2 .For Natural Language Processing tasks, RNN and LSTM models BID8 BID1 are widely used, which are different network architectures from the twodimensional CNN. However , the recent work of Super Characters method BID4 BID7 using twodimensional word embedding achieved state-of-the-art result in text classification and sentiment analysis tasks, showcasing the promise of this new approach. The Super Characters method is a two-step method. In the first step, the characters of the input text are drawn onto a blank image, so that an image of the text is generated with each of its characters embedded by the pixel values in the two-dimensional space. The resulting image is called the Super Characters image. In the second step, the generated Super Characters image is fed into a twodimensional CNN models for classification. The two- dimensional CNN models are trained for the text classification task through the method of Transfer Learning, which finetunes the pretrained models on large image dataset, e.g. ImageNet BID0 , with the labeled Super Characters images for the text classsification task.In this paper, we implemented NLP applications on mobile devices using the Super Characters method on a CNN-DSA chip as shown in Figure 1 . It takes arbitrary text input from keyboard connecting to a mobile device (e.g. Raspberry Pi). And then the text is pre-processed into a Super Characters image and sent to the CNN-DSA chip to classify. After post-processing at the mobile device, the final result will be displayed on the monitor. We implemented efficient on-device NLP applications on a 300mw CNN-DSA chip by employing the twodimensional embedding used in the Super Characters method. The two-dimensional embedding converts text into images, which is ready to be fed into CNN-DSA chip for two-dimensional CNN computation. The demonstration system minimizes the power consumption of deep neural networks for text classification, with less than 0.2% accuracy drop from the original VGG model. The potential use cases for this demo system could be the intension recognition in a local-processing smart speaker or Chatbot.
Deploy text classification and sentiment analysis applications for English and Chinese on a 300mW CNN accelerator chip for on-device application scenarios.
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