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
ontologies. The second demo classifies the Chinese online-shopping review into
positive or negative.

使用 CNN-DSA 芯片实现了在移动设备上的文本分类和情感分析应用，其采用一位和三位精度的系数以及五位激活表示，功耗低于 300mW，并在 CNN-DSA 芯片内逼近外部完全连接层。此外，还进行了两个 NLP 任务的系统演示，分别为将英文维基百科句子分类到 14 个本体中和将中文在线购物评论分类为正面或负面。