Artificial Intelligence & Machine Learning Developer Journey

Applications of Machine Learning using Silicon Labs’ SoCs

Silicon Labs' Wireless SoCs support a range of ML applications, such as sensor signal processing for predictive and preventative maintenance, bio-signal analysis for healthcare, and cold chain monitoring. They also enable audio pattern matching for security applications, voice commands for smart device control, and low-resolution vision for tasks like people counting and presence detection. The SoCs offer various RAM sizes to accommodate different application requirements. Machine learning models are applied to data from sensors such as microphones, cameras, and those measuring time-series data like acceleration and temperature. These models include audio pattern matching, wake word/command word detection, fingerprint reading, always-on vision, and image/object classification and detection. The detected events can then be further processed according to the requirements.

1. Buy Kit: Hardware and Examples

Silicon Labs provides offers several development and explorer kits, from ultra-low-cost, small form factor to compact, feature-packed platforms designed for robust networks. We have several exciting demos, including wake-word detection, Pacman, and gesture control. These feature-rich kits support multiple protocols and come in different memory configurations with a wide variety of sensors and peripherals for quick debugging and rapid prototyping. Based on the demos you are interested in, please select the kit that best fits your needs below. The demos are hardware agnostic.

2. Create User Accounts

While you wait for your Development Kit, we recommend setting up your user accounts.

4. Explore Demos

Here is a list of some additional ideas which could be easily realized with minimal coding, modifying the referenced example application as suggested below. These use cases are not provided as ready-to-go demos; instead they provide a perfect context for further evaluation.

1. Build Model

Already have your .tflite file ready to go? Skip to the next step: "Test and Validate" .

Train your model and prepare it for conversion into a deployable format.

If you are looking for a pre-built Machine Learning solution - jump to the last tab "Pre-Built Solution"

2. Test and Validate

Evaluate your model's performance against the embedded target, validate the model to ensure it meets required performance metrics.

Optional Tool: MLTK Model Profiler

The MLTK model profiler provides information about how efficiently a model may run on an embedded target. The model profiler allows for executing a .tflite model file in a simulator or on a physical embedded target.

Note: This tool is optional and not officially supported by Silicon Labs, yet.

3. Deploy Model

Integrate and deploy your validated model onto the embedded device.

• Add AI/ML SDK Extension
• Configure TensorFlow Micro Component in Studio: set up the component to select the correct kernel for your embedded device
  
• Include and Run the Model: copy your .tflite model into your application into the config folder in your Simplicity Project
  
• Implement Post-Processing: add any necessary post-processing steps to handle model’s output and integrate it with your application’s logic
  

Turn Key Solutions

Pre-built, ready-to-deploy AI/ML solutions on Silicon Labs SoCs that simplify the development process and accelerate time-to-market.

Design Partners

Silicon Labs has pre-screened and certified the following third-party AI/ML design service companies
to help you design and develop your customized AI/ML solution.