Low-Power Audio Keyword Spotting Using Tsetlin Machines

The emergence of artificial intelligence (AI) driven keyword spotting (KWS) technologies has revolutionized human to machine interaction. Yet, the challenge of end-to-end energy efficiency, memory footprint and system complexity of current neural network (NN) powered AI-KWS pipelines has remained ev...

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Bibliographic Details
Published in:Journal of low power electronics and applications 2021-06, Vol.11 (2), p.18
Main Authors: Lei, Jie, Rahman, Tousif, Shafik, Rishad, Wheeldon, Adrian, Yakovlev, Alex, Granmo, Ole-Christoffer, Kawsar, Fahim, Mathur, Akhil
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Architecture
Artificial intelligence
Boolean
Complexity
Design
Embedded systems
Energy conversion efficiency
Energy efficiency
Feedback
Internet of Things
keyword spotting
Keywords
learning automata
Machine learning
MFCC
Neural networks
pervasive AI
speech command
Tsetlin Machine
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Summary:The emergence of artificial intelligence (AI) driven keyword spotting (KWS) technologies has revolutionized human to machine interaction. Yet, the challenge of end-to-end energy efficiency, memory footprint and system complexity of current neural network (NN) powered AI-KWS pipelines has remained ever present. This paper evaluates KWS utilizing a learning automata powered machine learning algorithm called the Tsetlin Machine (TM). Through significant reduction in parameter requirements and choosing logic over arithmetic-based processing, the TM offers new opportunities for low-power KWS while maintaining high learning efficacy. In this paper, we explore a TM-based keyword spotting (KWS) pipeline to demonstrate low complexity with faster rate of convergence compared to NNs. Further, we investigate the scalability with increasing keywords and explore the potential for enabling low-power on-chip KWS.
ISSN:2079-9268
2079-9268
DOI:10.3390/jlpea11020018