AI toolkit works with MCU open-source neural network inference engine

SensiML Corporation announced that its SensiML Analytics Toolkit now seamlessly integrates with Google’s TensorFlow Lite for Microcontrollers. Developers working with Google’s TensorFlow Lite for Microcontrollers open source neural network inference engine now have the option to leverage SensiML’s powerful automated data labeling and preprocessing capabilities to reduce dataset errors, build more efficient edge models, and do so more quickly.

Following the standardized workflow within the SensiML model building pipeline, developers can collect and label data using the SensiML Data Capture Lab, create data pre-processing and feature engineering pipelines using the SensiML Analytics Studio, and perform classification using TensorFlow Lite for Microcontrollers. The net result is a state-of-the-art toolkit for developing smart sensor algorithms capable of running on low power IoT endpoint devices.

TensorFlow Lite for Microcontrollers is a version of TensorFlow Lite from Google, which has been specifically designed to implement machine learning models on microcontrollers and other memory-limited devices. SensiML, via its SensiML Analytics Toolkit, delivers the easiest and most transparent set of developer tools for the creation and deployment of edge AI sensor algorithms for IoT devices. Through this tightly coupled integration of SensiML and Google’s TensorFlow, developers reap the benefit of best-in-class solutions for building intelligent sensor AI algorithms capable of running autonomously on IoT edge devices.

The SensiML Analytics Toolkit with support for TensorFlow Lite for Microcontrollers is available now from SensiML.