SensiML Integrates Google's TensorFlow Lite for Microcontrollers

- Provides users of TensorFlow Lite for Microcontrollers with powerful, automated tools for dataset management and feature preprocessing

- ­Integrated workflow enables higher quality neural network models, built faster, and more efficiently wherever IoT edge sensor algorithms are needed

PORTLAND, Ore., Sept. 16, 2020 /PRNewswire/ -- SensiML™ Corporation, a leading developer of
AI tools for building intelligent IoT endpoints, today 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.

"Without high-quality datasets, it is difficult to train models that can perform reliably in production. We talk a lot about how critical datasets are to building sensor-based edge AI applications, but currently don't have a good solution for creating them," said Pete Warden technical lead of Google's TensorFlow Mobile Team. "SensiML's Data Capture Lab fills this gap in the industry by providing tools to quickly collect and annotate high-quality sensor datasets."   

"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," said Ian Nappier, TensorFlow Lite for Microcontrollers product manager at Google.

"TensorFlow Lite for Microcontrollers is an industry-leading framework for running neural network models on embedded devices," said Chris Rogers, chief executive officer of SensiML.  "SensiML's end-to-end workflow automates the tedious and error-prone data cleansing and labeling process as well as preprocessing and feature extraction providing a powerful new capability for TensorFlow Lite for Microcontrollers users to improve performance and productivity for their edge AI projects."

The SensiML Analytics Toolkit with support for TensorFlow Lite for Microcontrollers is available now from SensiML. For more information, visit

About SensiML
SensiML, a subsidiary of QuickLogic (NASDAQ: QUIK), offers cutting-edge software that enables ultra-low power IoT endpoints that implement AI to transform raw sensor data into meaningful insight at the device itself. The company's flagship solution, the SensiML Analytics Toolkit, provides an end-to-end development platform spanning data collection, labeling, algorithm and firmware auto generation, and testing. The SensiML Toolkit supports Arm® Cortex®-M class and higher microcontroller cores, Intel® x86 instruction set processors, and heterogeneous core QuickLogic SoCs and QuickAI platforms with FPGA optimizations. For more information, visit

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SOURCE SensiML Corporation