Building upon our release last week of support for step-by-step tutorial showing how SensiML data collection, annotation, and feature preprocessing is combined with a classifier using TFL Micro., this week we’ve added a
The tutorial is built around an example application and dataset for a smart boxing glove wearable integrating aclass MCU with 6-axis accel+gyro to provide realtime punch recognition.
Fitting we believe, as the combination punch of SensiML and Googleshould be a real knockout! <Ed note: Sorry I couldn’t resist.>
Original Post (from 9/16/20):
Today we pulled the covers off our latest update of which we are quite excited and suspect both SensiML and TFL users will be as well.
As of this week,now provides pipeline support for one of the most popular AI frameworks in existence: TensorFlow, specifically its variant .
The combination of SensiML andoffers best-in-class AI code generation for applications from consumer wearables to multi- industrial monitoring devices. provides developers with a production-class tool for AI management and preprocessing. Google’s provides a popular well known subset of the TensorFlow framework for building and deploying code on small microcontrollers.
To learn more about the integration and what it can do for your TensorFlow Lite Users page.project, see the
“The integration with SensiML Analytics Studio and Google’s TensorFlow Lite for Microcontrollers provides a nice end-to-end workflow for creating IoT edge models on embedded devices”Ian Nappier, TensorFlow Lite for Microcontrollers Product Manager at Google