1 research outputs found
System Demo for Transfer Learning across Vision and Text using Domain Specific CNN Accelerator for On-Device NLP Applications
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.Comment: Four pages, four figures, one table. Accepted by IJCAI2019 Tusion
Worksho