An example of a complete smart toy application built around on-device, ML-based gesture recognition using the M5StickC PLUS.
SensiML @ Silicon Labs Works With 2022
Watch the video of Chris Rogers’ conversation with Ross Sabolcik, VP/GM of Industrial and Commercial IoT products at Silicon Labs, about the benefits of AI at the edge, key use cases, and how Silicon Labs and SensiML are working together to advance IoT smart sensing with ML.
Infineon “Build AI for the IoT” Hackster.io Contest
Winner of the Infineon Build AI for the IoT contest selected SensiML for delivering edge AI for their application.
SensiML and Infineon Team Up to Add AI for PSoC™ 6 MCUs
We just announced that SensiML has teamed up with Infineon Technologies AG to deliver a complete AI/ML solution for the Infineon PSoC™ 6 family of microcontrollers (MCUs) and XENSIV™ sensors. This is great news for Infineon customers as they can now use SensiML’s tools to easily create intelligent IoT endpoints using the ultra-low power and
Smart Manufacturing with SensiML
Opportunities to improve manufacturing processes by adding machine learning sensors at the IoT edge are rapidly emerging. Thanks to a combination of Powerful microcontrollers and multi-core SoCs like the EOS S3 on QuickLogic’s QuickAI HDK High-resolution, low-cost MEMS sensors and microphones Powerful AutoML-based AI tools like SensiML Analytics Toolkit it is possible to bring sophisticated
Congratulations to Our Climate Change Challenge Winners!
About six months ago we, the folks at QuickLogic, and the Avnet Hackster.io online community announced our Challenge Climate Change contest. The idea was to encourage creative smart technology projects that improve awareness, change behavior, or optimize processes to impact climate change for the better. Towards that end, we opened up the contest to developers
SensiML’s New Open Source Initiative: AI Transparency and Flexibility Supporting IoT Sensor Products for the Real-World
Today SensiML took a leadership position in AI/ML tools for the IoT edge by announcing our new Open Source Initiative. SensiML Open Source Embedded SDK (coming later this summer) – The full library of SensiML segmenters, transforms, features, and classifiers as implemented by the AutoML and Python-based SensiML Analytics Toolkit Notebook will become available in open source format.
SensiML Tutorial Series
Webinar tutorial series discussing a variety of topics related to embedded IoT development, TinyML and AI at the edge, sensor data processing, and the application of SensiML Analytics Toolkit.
Join Us at the TinyML Summit
SensiML will be presenting on Thursday, March 25th at 12 noon. We will cover how to build smart IoT products with production-quality AI tools.
Using Machine Learning to Tackle Climate Change: A HacksterIO Contest
Climate change: A huge challenge forcing us all to be smarter about how we utilize our planet’s resources. At SensiML we’re all about smarter, which is why we’re teaming up with QuickLogic and Hackster.io to create the “Challenge Climate Change” contest with over $70k in prizes. So do some good, learn about edge AI, and maybe win big!
Visit Us at the ST Developers Conference 2020: October 20th and 21st – An Online Event
Join SensiML on October 20 & 21 at the ST Developers Conference, this year being held entirely online. This year SensiML is pleased to be amongst the lead event sponsors and will be delivering an on-demand session presentation as well as demoing SensiML Analytics Toolkit in our virtual booth. Event registration is free and open now. SensiML will be presenting throughout the two-day event. We hope to “see” you there!
UPDATE: SensiML Integrates Google’s TensorFlow Lite for Microcontrollers
SensiML Analytics Toolkit now provides pipeline support for one of the most popular open-source AI frameworks in existence: TensorFlow, specifically its TinyML variant TensorFlow Lite for Microcontrollers. This week we’ve added a step-by-step boxing wearing tutorial showing how SensiML data collection, annotation, and feature preprocessing are combined with a neural network classifier using TFL Micro.