Embedded AI and TinyML for on-device inference
On-device AI can reduce latency, bandwidth and privacy concerns, but models must respect memory, power and real-time constraints. Silicon LogiX supports embedded AI from feasibility to deployment.
AI that fits the device
The challenge is not just training a model. It is making inference reliable within memory, latency, power and update constraints.
- Audio classification, anomaly detection, vision and signal analysis.
- TinyML and quantized models for MCUs and edge SoCs.
- Model integration with firmware, sensors and data acquisition pipelines.
- Evaluation of latency, memory, accuracy and deployment trade-offs.
What it includes
Understand whether on-device inference is technically and economically sensible.
Quantization, runtime integration and firmware-side data preparation.
Where inference runs, how results are used and how updates are managed.
Measure accuracy, latency, memory and behavior on real target hardware.
Working method
- Review goals, constraints, existing code or hardware documentation.
- Define risks, architecture choices and a practical execution plan.
- Work iteratively on real targets, with measurable checkpoints.
- Deliver code, documentation and technical decisions that the team can maintain.
Related guides and pages
A case study for bare-metal audio inference.
From prototype to production constraints.
Edge AI without sending everything to the cloud.
Why inference is moving closer to devices.
Frequently asked questions
Do you train models?
The focus is embedded deployment and product integration; model training can be supported when it is part of the technical path.
Can AI run on a microcontroller?
Yes for specific models and signals, provided memory, latency and accuracy targets are realistic.
Can you evaluate if AI is worth it?
Yes. Feasibility work can compare AI against simpler deterministic approaches.