Silicon Labs and Edge Impulse announced 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. Studies have shown that 87% of data science projects never reach full production, often due to artificial intelligence/ML implementation challenges. 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 quickly create neural networks across a wide range of Silicon Labs products for free, with integrated deployment to Simplicity Studio. By embedding 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.