Definition #
Edge AI involves running artificial intelligence models locally on end-user devices (cameras, sensors, phones) instead of relying on cloud servers.
Key Characteristics #
- Low-latency processing (10-100x faster than cloud)
- Works offline without internet
- Privacy-preserving (data stays on-device)
Why It Matters #
Enables real-time decisions in critical applications—autonomous vehicles process sensor data locally at <10ms latency vs 200ms+ for cloud roundtrips.
Common Use Cases #
- Smart factory predictive maintenance
- Augmented reality filters
- Wearable health monitors
Examples #
- TensorFlow Lite for Microcontrollers
- NVIDIA Jetson edge AI kits
- Apple’s Neural Engine (iPhone chips)
FAQs #
Q: Does Edge AI require specialized hardware?
A: While possible on CPUs, GPUs/TPUs/NPUs significantly boost performance.
Q: How is model size handled?
A: Techniques like quantization (e.g., converting 32-bit to 8-bit numbers) reduce model footprints by 4x.