01
Edge Intelligence
Edge AI performs inference near the source of data rather than sending every observation to a remote server. This can reduce latency, bandwidth use, and dependence on network availability.
02
Embedded AI Constraints
- Limited RAM.
- Limited flash/storage.
- Restricted compute throughput.
- Energy constraints.
- Thermal limits.
- Real-time latency requirements.
- Intermittent connectivity.
03
Model Optimization
- Quantization reduces numerical precision.
- Pruning removes selected parameters or structures.
- Knowledge distillation transfers behavior to smaller models.
- Architecture search can optimize models for target hardware.
- Hardware accelerators can execute neural-network operations efficiently.
04
Applications
- Visual inspection.
- Wake-word detection.
- Predictive maintenance.
- Gesture recognition.
- Robotic perception.
- Sensor anomaly detection.
- Wearable intelligence.
- Autonomous devices.