Embien builds machine learning on embedded devices for teams who need real-time inference on hardware with kilobytes of RAM, not a cloud GPU. Our engineers take a trained model through quantization, optimization, and embedded machine learning for IoT devices deployment — on microcontrollers, not just edge servers.
TinyML sounds simple until a model that ran fine on a laptop has to fit in 256KB of flash and run on a coin-cell battery. That's the real engineering problem behind machine learning on embedded devices: shrinking a model, choosing the right ML frameworks for edge deployment, and validating accuracy against real sensor noise on the actual target MCU.
Embien has delivered embedded machine learning for IoT devices in production — most recently a water leakage detection system built on a TinyML framework, running continuous on-device inference on ultra-low-power hardware in the field. For the framework landscape behind that work, see our Insight on embedded AI frameworks and TinyML
Four proof points buyers evaluating machine learning on embedded devices ask about before committing to a framework and an MCU.
Quantization, pruning, and compression so a trained model fits real embedded memory and compute budgets without losing usable accuracy.
Wire the optimized model into production firmware — sensor pipelines, inference scheduling, and power management — for embedded machine learning for IoT devices that runs reliably in the field.
Selection and integration of TensorFlow Lite Micro, CMSIS-NN, and other embedded-first ML frameworks for edge deployment, matched to your MCU or SoC.
Port and re-validate models across microcontroller families so machine learning on embedded devices isn't locked to a single silicon vendor.
Explore the machine learning on embedded devices capabilities behind every TinyML engagement, from framework selection through field deployment.
Design and train models sized from the start for ultra-low-power devices, not shrunk after the fact from a cloud-scale architecture.
Integrate ML frameworks for edge deployment — runtime selection, hardware acceleration, and inference pipeline tuning — for production edge AI.
Field-proven deployment discipline, demonstrated in our own TinyML on-device machine learning for ultra-low-power devices case study.
Share your model, target MCU or SoC, and power budget. We'll help you scope TinyML on-device machine learning for ultra-low-power devices, from framework choice to field validation.
It lets a device make sensor-level decisions — anomaly detection, classification, predictive triggers — without a constant round trip to the cloud, which cuts latency, bandwidth, and power draw at once.
Tell us about your model, target hardware, and power budget — an engineer will follow up.