Edge AI · intelligence at the edge

AI where the data is generated — directly on the device.

Not every analysis belongs in the cloud. With embedded machine learning we bring models directly onto microcontrollers and edge devices: low latency, low cost, data-minimizing — and operational without a constant connection.

Discuss your Edge AI project

TinyML
On-device inference
low latency
data-minimizing
ARM Cortex-M / NPU

Edge AI shows its strengths where the cloud is too slow or too expensive.

Real-time response, functionality without a network, no sensitive raw data in the cloud: embedded ML is ideal for machines, ECUs and products in the field. Because we consider hardware, firmware and model together, the model fits the real device.

Model, firmware and hardware from a single source.

From collecting the training data through the lean model to integration into the embedded software.

Frequently asked questions.

Does AI really run on a microcontroller?

Yes — with quantized, lean models (TinyML), classification, anomaly detection and pattern recognition run on Cortex-M-class devices or small NPUs. The key is tailoring the model to the available resources.

What is the difference from cloud AI?

Edge AI computes locally: faster, data-minimizing, offline-capable. Cloud AI offers more computing power for training and aggregation. The two are often combined — training/updates in the cloud, inference on the device.

Related services.

Bring intelligence onto the device?

Tell us your application and target hardware — we'll assess what is feasible on-device.

Meine
Harbke

info@samd-solutions.de
+49 1511 0988717

Harbke — Gesellschaftssitz & EMV-Labor mit Vollabsorberhalle · Am Glüsig 1C, 39365 Harbke

Meine — Technische Entwicklung (Elektronik & Embedded) · Peiner Straße 16, 38527 Meine

Mo–Fr 07:00–19:00 Uhr