Author:

Keywords:

Science & Technology, Technology, Engineering, Electrical & Electronic, Engineering, LSTM, Keyword Spotting, Deep Learning, Speech Recognition, Digital Accelerator

Abstract:

© 2018 IEEE. The ubiquitous importance of speech recognition for diverse applications in mobile devices, necessitates its low power embedded execution. Often, a Keyword Spotting System (KWS) is used to detect specific wake-up words spoken by a user, as a simple user interface, or front-end layer to a larger speech recognition system. Yet, such KWS must be always active, hence imposing strict power and latency constraints. While deep learning algorithms like Long Short-Term Memory (LSTM) demonstrated excellent KWS accuracies, current implementations fail to fit in the tight embedded memory and power budgets. This paper presents Laika: the implementation of a KWS system using an LSTM accelerator designed in 65nm CMOS. For this application, an LSTM model is trained through a speech database and deployed on our custom, yet highly programmable LSTM accelerator. Approximate computing techniques further reduce power consumption, while maintaining high accuracy and reliability. Experimental results demonstrate a power consumption of less than 5μW for real-time KWS applications.