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Evolve

Embedded AI: machine learning on the device

Models that run where the data is produced — on microcontrollers and edge processors — optimised for memory, power and latency.

· by BCF Embedded engineering team

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Why edge, not cloud

Latency

Decisions are made on the device, without a round trip to a server.

Energy and transmission

Sending results instead of raw data saves radio time and battery.

Privacy

Sensitive data such as audio or images can stay on the device.

By data type

Time series and sensors

Vibration, current, temperature: anomaly detection and condition monitoring.

Audio

Keyword spotting, sound event detection, voice activity detection.

Vision

Object detection and visual inspection on processors with enough compute.

By hardware class

The hardware class sets what is possible. We help choose it before the model is designed.

Tiny Edge

Hardware
Cortex-M0/M4 class, < 256 KB RAM
Power
Coin cell / energy harvesting
Typical use
Simple anomaly detection, wake-word triggers

Small Edge

Hardware
Cortex-M7/M33/M55 class, 512 KB – 2 MB RAM
Power
Small batteries
Typical use
Keyword spotting, sensor fusion

Mid Edge

Hardware
MPUs with NPU, 512 MB – 4 GB RAM
Power
Mains / large battery packs
Typical use
Real-time video analytics, gateway AI

Heavy Edge

Hardware
Edge GPUs and industrial PCs, > 4 GB RAM
Power
Mains, active cooling
Typical use
Multi-camera video, larger models

Tools

Frameworks

  • TensorFlow Lite Micro
  • Edge Impulse
  • STM32Cube.AI

Languages

  • Python
  • C / C++

Quantization and optimization

Most models are trained in floating point and must be converted before they fit a microcontroller. We quantise models (typically to INT8), prune where it helps and measure accuracy, memory and latency on the target hardware — not only in simulation.

Use cases

Predictive maintenance is the closest use case for our focus sectors: detecting changes in vibration or current of pumps, motors and power electronics in maritime and energy installations.

FAQ

Discuss your project

Describe your device and goal. An engineer replies within one business day.

Prefer e-mail?

[email protected]
  • Reply within one business day
  • NDA available before any technical deep-dive

Our offices

Opole (Headquarters): ul. Technologiczna 2, 45-837 Opole

Wrocław (Office): ul. Strzegomska 42B, 53-611 Wrocław

Warsaw (Office): ul. Żurawia 6/12, 00-503 Warsaw

BCF Embedded by Bright Coders' Factory (home page)

Firmware, hardware and security engineering for connected devices — from PoC to production.


BCF Software Sp. z o.o.

ul. Technologiczna 2, 45-837 Opole, Poland

NIP: PL 754 31 26 298 · KRS: 0000634606 · REGON: 365280382

[email protected]

Offices

Opole (Headquarters) — ul. Technologiczna 2, 45-837 Opole

Wrocław (Office) — ul. Strzegomska 42B, 53-611 Wrocław

Warsaw (Office) — ul. Żurawia 6/12, 00-503 Warsaw

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