A Resource-Efficient Keyword Spotting System Based on a One-Dimensional Binary Convolutional Neural Network

Abstract

This paper proposes a resource-efficient keyword spotting (KWS) system based on a convolutional neural network (CNN). The end-to-end KWS process is performed based solely on 1D-CNN inference, where features are first extracted from a few convolutional blocks, and then the keywords are classified using a few fully connected blocks. The 1D-CNN model is binarized to reduce resource usage, and its inference is executed by employing a dedicated engine. This engine is designed to skip redundant operations, enabling high inference speed despite its low complexity. The proposed system is implemented using 6895 ALUTs in an Intel Cyclone V FPGA by integrating the essential components for performing the KWS process. In the system, the latency required to process a frame is 22 ms, and the spotting accuracy is 91.80% in an environment where the signal-to-noise ratio is 10 dB for Google speech commands dataset version 2.