2020/09/09 by Peter Blouw, Gurshaant Malik, Blouw, Peter +7 · 2 citations
Computer Science · #Advanced Text Analysis Techniques #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Music and Audio Processing #Signal Processing (eess.SP) #Sound (cs.SD) #Speech Recognition and Synthesis #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2009.04465
openalex publication_date 2020/09/09 · openalex created_date 2020/09/14 · openalex updated_date 2026/07/28
Keyword spotting (KWS) provides a critical user interface for many mobile and edge applications, including phones, wearables, and cars. As KWS systems are typically 'always on', maximizing both accuracy and power efficiency are central to their utility. In this work we use hardware aware training (HAT) to build new KWS neural networks based on the Legendre Memory Unit (LMU) that achieve state-of-the-art (SotA) accuracy and low parameter counts. This allows the neural network to run efficiently on standard hardware (212μW). We also characterize the power requirements of custom designed accelerator hardware that achieves SotA power efficiency of 8.79μW, beating general purpose low power hardware (a microcontroller) by 24x and special purpose ASICs by 16x.