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Power-efficient CNN Domain Specific Accelerator (CNN-DSA) chips are currentlyavailable for wide use in mobile devices. These chips are mainly used incomputer vision applications. However, the recent work of Super Charactersmethod for text classification and sentiment analysis tasks usingtwo-dimensional CNN models has also achieved state-of-the-art results throughthe method of transfer learning from vision to text. In this paper, weimplemented the text classification and sentiment analysis applications onmobile devices using CNN-DSA chips. Compact network representations usingone-bit and three-bits precision for coefficients and five-bits for activationsare used in the CNN-DSA chip with power consumption less than 300mW. For edgedevices under memory and compute constraints, the network is further compressedby approximating the external Fully Connected (FC) layers within the CNN-DSAchip. At the workshop, we have two system demonstrations for NLP tasks. Thefirst demo classifies the input English Wikipedia sentence into one of the 14ontologies. The second demo classifies the Chinese online-shopping review intopositive or negative.

System Demo for Transfer Learning across Vision and Text using Domain Specific CNN Accelerator for On-Device NLP Applications