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Silicon Labs – Works With 2023

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Silicon Labs is proud to present the fourth annual Works With – a virtual developer conference for building the skills needed to create impactful connected devices.

Works With – Where Innovation Meets Implementation

Works With 2023 brings you two days of technical sessions, keynotes, and expert-led panels covering the latest trends, tools, and design techniques for developers. This free, all-virtual event features more than 40 technical sessions specifically designed to bring together leading ecosystem partners, thought leaders, and Silicon Labs experts to dive deep into the technologies driving IoT.

This is your chance to learn from Silicon Labs experts, design partners and developers from around the world. Select the sessions that interest you to create your own personal agenda.

Register Now For Your Free Place

Conference Tracks

Wi-Fi
Provides developers with the knowledge to integrate Wi-Fi into their applications while managing coexistence, designing for power and security requirements, as well as navigating emerging certification standards.

Matter
Developers, manufacturers and brands driving the Matter standard forward will share the latest developments and insights for its future in the smart home and beyond.

Bluetooth
Provides developers with the knowledge to integrate Bluetooth into their applications while managing coexistence, designing for power and security requirements, as well as navigating emerging certification standards.

AI/ML
This year’s agenda features sessions on developing successful AI/ML applications, predictive learning techniques, and building ML into IoT applications with Silicon Labs components.

LPWAN
Our Works With 2023 LPWAN track will include sessions on developing smart city and large-scale outdoor applications, advanced features to look for in sub-GHz networks, and more.

IoT Trends
RF design is complex, with new technologies, techniques, and tools being introduced every day and our IoT Trends track will focus on topics ranging from security to machine learning.