Silicon Labs, a provider of silicon, software and solutions for a smarter, more connected world, and Edge Impulse, a development platform for machine learning on edge devices, announce a collaboration to enable rapid development and deployment of machine learning (ML) on Silicon Labs EFR32 wireless SoCs and EFM32 microcontrollers (MCUs). Implementation of the Edge Impulse tool enables complex motion detection, sound recognition and image classification on low-power, memory-constrained, and remote edge devices.
This new collaboration between Silicon Labs and Edge Impulse enables device developers to generate and export the ML models directly to the device or Simplicity Studio, the integrated development environment from Silicon Labs, with the click of a button, implementing machine learning in minutes.
Edge Impulse allows developers to create neural networks across a wide range of Silicon Labs products for free, with integrated deployment to Simplicity Studio. By embedding state-of-the-art TinyML models on EFR32 and EFM32 devices, such as MG12, MG21 and GG11, the solution enables:
- Machine learning
- Real-world sensor data collection and storage
- Advanced signal processing and data feature extraction
- Deep Neural Network (DNN) model training
- Deployment of optimized embedded code
The Edge Impulse tool also leverages Edge Impulse's Edge Optimized Neural (EON) technology to optimize memory use and inference time.