Machine learning in IoT refers to running trained models directly on connected devices to process data from sensors, microphones, and cameras — enabling real-time decisions for use cases like voice recognition, vision processing, anomaly detection, and more, all without relying on the cloud.

Running machine learning models on IoT devices can reduce power consumption when using a hardware accelerator like Silicon Labs’ Matrix Vector Processor. For compute-heavy tasks such as matrix operations, the MVP can deliver up to 8x faster inference and up to 6x lower energy use compared to running the same model on the CPU, especially when dealing with larger workloads. This allows the CPU to remain idle or sleep, improving overall energy efficiency. 

No, you do not need internet access to run machine learning on IoT devices. With on-device inferencing and hardware acceleration, models can process data locally at the edge — enabling fast, reliable, and private IoT machine learning without relying on the cloud. 

You can get started in minutes using our ML development kits, which come with out-of-the-box demos — including a voice-controlled Pac-Man game. Check out our AI/ML Developer Journey for a step-by-step guide 

IoT with machine learning powers a wide range of applications including voice recognition, gesture detection, vision processing, predictive maintenance, anomaly detection, and smart access — all running efficiently on-device without cloud dependency.

Yes — you absolutely can. Our platform supports integration of pretrained models from popular frameworks, and our certified AI/ML partners offer ready-to-deploy solutions optimized for Silicon Labs hardware. Use them directly or finetune for your specific IoT machine learning application.

Silicon Labs offers a range of tools to help you evaluate and optimize machine learning in IoT devices. For example, the Model Profiler estimates memory usage and inference time on your target hardware, while the Model MVP Compiler (built into Simplicity Studio) optimizes model execution for efficient edge inferencing. These tools make it easy to fine-tune performance before deployment. 

No. Silicon Labs partners with trusted AI/ML providers who offer pre-built, ready-to-deploy solutions on our SoCs — ideal for teams looking to get started quickly. For custom applications, we also work with certified design service partners who can help you develop and deploy tailored IoT machine learning solutions without needing deep ML expertise.