Are you based in Silicon Valley and interested in learning more about any or all of the following?
- Using Piccolo AI and SensiML’s other tinyML software tools on Silicon Labs EFR32 MCUs
- Silicon Labs MVP accelerator enabled SoCs for AI/ML workloads on IoT devices
- Implementing audio recognition tasks using SensiML and SiLabs’ BG24/MG24 SoCs
If you answered yes to any of the above, you should have a look at Silicon Labs upcoming in-person event in San Jose, CA September 19th, 2024.
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Register for the Event
Amongst many other topics, SensiML will be on-hand and featured in the following AI/ML sessions:
ML101: Machine Learning at the Edge
September 19, 2024, 9:00 AM-9:55 AM PT, San Martin
Discover the innovative capabilities of our wireless SoCs with built-in AI/ML hardware accelerators. In this session, we’ll explore how the accelerator streamlines inferencing at the edge, conserving power by offloading processing, and enabling edge computing, thus reducing latency and minimizing false positives. The session will conclude with a preview of our upcoming partner session with SensiML, where we together will explore various ML models and the practical applications they enable when integrated with our AI/ML accelerator.
ML301: Hands-on Workshop: Easily Create Intelligent IoT Sensing Devices with SensiML and SiLabs AI Accelerated SoCs
September 19, 2024, 1:00 PM-2:25 PM PT, San Martin
Join us for this workshop session where we will demonstrate the start-to-finish workflow for creating an autonomous machine learning algorithm that runs locally on the Silicon Labs MG24. By the end of the session, you will gain valuable understanding for the following ML development concepts:
- How to properly setup data collection for building AI/ML train/test datasets
- The process as well as key tips for scalable data capture and labeling suitable for production models
- AutoML model generation and tuning
- Effective model test and validation techniques
- Generation of power and memory optimized device-level ML firmware libraries
- Integration and testing of ML firmware within an IoT device application
As we work through the above concepts, we will utilize the MG24 development kit and SensiML’s Analytics Toolkit and introduce the latest SensiML open-source offering Piccolo AI.