Internet of Things (IoT) & Embedded Systems Architecture
Edge AI on Microcontrollers
TinyML runs small models directly on microcontrollers, enabling vibration, audio, and vision inference without sending raw data to the cloud. The constraints are severe: kilobytes of RAM, limited compute, and tight power budgets. Success comes from model design and quantization, not from porting a large model downward.
Puntos clave
- Start from the sensor and label quality, since model architecture cannot fix bad data.
- Quantize models to int8 and measure accuracy loss against the original.
- Profile RAM and flash early, including the runtime and operator overhead.
- Use hardware acceleration where the silicon provides it, such as DSP or NPU blocks.
- Design for on-device confidence thresholds and fallback behavior.
- Test inference under temperature and voltage variation, not only on the bench.
- Version models and datasets so field behavior can be reproduced.
- Measure energy per inference, which often matters more than latency.
Preguntas frecuentes
Can a microcontroller run a neural network?
Yes, small quantized models run comfortably on modern MCUs.
How much accuracy is lost with quantization?
Usually a small amount, but it depends on the model and calibration data.
Do I need a specialized framework?
TensorFlow Lite for Microcontrollers and vendor toolchains cover most cases.
When should inference stay in the cloud?
When models are large, data is rich, or accuracy requirements exceed what the device can support.
Cómo ayuda Kinetikatech con internet of things (iot) & embedded systems architecture
Esto es lo que hacemos cada día: Edge gateways, real-time operating systems, signed firmware updates, and power budgets, the engineering that keeps constrained devices correct for years without a technician on site. Nuestro servicio Gestión de servidores y Seguridad de servidores Linux lo cubre de principio a fin: evaluación, implementación, monitoreo 24/7 y respuesta de emergencia en 30 minutos.