hands-on workshop series
MG24 Tech Lab
Watch the latest hands-on workshop series to learn about the advanced AI/ML capabilities of the EFR32MG24 wireless SoC while getting technical training the help of Silicon Labs' expert engineers.
Silicon Labs Can Help You Bring ML to the Tiny Edge
ML at the Edge
To better navigate the need for lower power consumption and size constraints in consumer devices, small, integrated development solutions are needed.
ML for Any Wireless Application
Bring ML to any application with a full portfolio of multiprotocol SoCs, a wide selection of development tools, and extensive expertise across wireless standards.
ML as a Feature
Machine learning offers device manufacturers across industries and applications a way to introduce feature-rich, low-power products that stand out in the market.
ML Development Tools for Anyone
Silicon Labs offers ML development tools for any level of experience and suited to your specific application. Depending on the tool, different sets of developer skills are required. To help determine which tool, the developer skills are grouped into three categories: ML Solutions, ML Explorer, and ML Experts.
ML Solutions
Developers at this level require very little, if any, experience with ML applications and look for solutions focused on their specific use case that they can integrate into their current application. The tools offered at this level will focus on using ML as a methodology but do not require any ML experience.
ML Explorer
An ML Explorer is an experienced embedded developer familiar with ML concepts but might be working on their first ML project or are exploring how ML can help them differentiate their product. Developers at this level are interested in a tool that offers end-to-end coverage of the workflow or prefer GUI-based tools over code-based solutions.
ML Expert
An ML Expert is someone with extensive experience working on ML projects and who is familiar with TensorFlow and Python. These developers understand how to pre-process raw data and attenuate the key elements, know how to create the proper network of convolutional computations, and how to interpret the constant output of stochastic information from inferencing.
Application Examples and Getting Started Resources
Below are some of the most common use cases for ML application development, as well as access to technical documentation that can help you select the software and tools that are most applicable. You will also find demos, tutorials, and examples according to your specific application and level of ML experience.
Getting Started With ML Software
Silicon Labs offers ML development tools for any level of experience, and we’ve curated our resources based on level of expertise:
Edge Impulse for ML Explorers
Edge Impulse is ushering in the future of embedded ML by empowering developers to create and optimize solutions with real-world data. Silicon Labs is making the process of building, deploying, and scaling embedded ML applications easier and faster than ever, unlocking massive value across every industry, with millions of developers making billions of devices smarter.
SensiML for ML Explorers
SensiML offers AutoML embedded code generation software for implementing AI at the IoT edge. The SensiML Analytics Toolkit supports rapid data collection, ML classification, and optimized firmware code generation, and built-in automation reduces development time and cost by generating optimized edge AI sensor algorithms in a fraction of the time.
TensorFlow for ML Experts
For ML development experts, we offer native support of TensorFlow Lite for Microcontrollers natively for all Series 1 and Series 2 wireless SoCs, and offer two options to create models:
Software for ML Solutions
For developers with little or no ML experience, Silicon Labs is building a set of partnerships for keyword and wake-word applications, as well as anomaly detection that require little ML expertise.
Coming Soon
AI/ML Partners
Getting Started With ML Hardware
Our EFR32xG24 and Thunderboard Sense Development Kits allow developers to load and run example projects on a target device. Both of these development kits run embedded applications that use the TensorFlow engine and have an integrated ML model. All of the software described above will run on either development kit.