Embedded AI
A technology that directly runs AI models inside devices to ensure real-time data processing and privacy without going through cloud servers.
Detailed explanation
Why It Matters in Tool Selection
The choice of Embedded AI tools (frameworks and hardware) is directly linked to the survival of a service. It is an essential consideration when building autonomous driving, medical device, and smart factory solutions that require real-time responses within 0.1 seconds while dramatically reducing cloud costs. In particular, with the strengthening of security regulations such as the EU Cyber Resilience Act (CRA) in recent times, local processing capability is becoming a key criterion for tool selection.
Checklist for Selection
- Whether the target hardware supports NPU/GPU acceleration (check TOPS performance)
- Usability of model compression tools (Quantization, Pruning)
- Inference efficiency relative to thermal design power (TDP)
- Ability to operate completely offline
- Support for secure boot and encryption to comply with security regulations (such as CRA)
Use Cases
Typical examples include smart door locks that recognize faces at the front door without an internet connection, predictive maintenance systems that immediately detect failures using vibration sensors in factories, and on-device AI assistants that process personal information without sending it to servers.