Keyword Spotting

This short TinyML demonstration shows a deep neural network running on an Arduino Nano 33 BLE Sense microcontroller performing keyword spotting. The DNN can distinguish 36 different spoken keywords (Pete Warden's Speech Commands dataset), infers at about 800ms per 1 second audio sample, and requires 190KB of ROM and 111KB of RAM.

Related Publications:

Energy-efficient Deployment of Deep Learning Applications on Cortex-M based Microcontrollers using Deep Compression

Deutel, M., Woller, P., Mutschler, C., & Teich, J. (2023, March). Energy-efficient Deployment of Deep Learning Applications on Cortex-M based Microcontrollers using Deep Compression. In MBMV 2023; 26th Workshop (pp. 1-12). VDE.

Dr.-Ing. Mark Deutel

Keyword Spotting

Related Publications:

Energy-efficient Deployment of Deep Learning Applications on Cortex-M based Microcontrollers using Deep Compression