A Real-Time Keyword Spotting System Based on an End-To-End Binary Convolutional Neural Network in FPGA

Abstract

This paper presents a real-time keyword spotting system in an FPGA. The proposed system performs the entire KWS task based on a binary convolutional neural network (BCNN) without involving any other complicated processing. The BCNN inference is efficiently carried out by skipping redundant operations. With all the essential components integrated, the proposed system has been implemented with only 8475 look-up tables in an FPGA. The proposed system processes one-second frame in 19.8 ms, exhibiting the spotting accuracy of 91.64%.