Why Edge AI Is Growing Fast
Latency, privacy, and connectivity constraints make cloud-only AI impractical for many use cases. Autonomous vehicles cannot tolerate 100ms round-trip times to a data center. Medical devices cannot send raw patient data to the cloud. Edge AI solves all three problems.
- Pruning — removes redundant weights. Structured pruning cuts entire filters, enabling hardware-efficient speedups.
- Knowledge distillation — trains a small student model to mimic a large teacher, transferring performance at a fraction of the compute cost.
- INT8/INT4 quantization — critical for edge deployment; converts FP32 weights to low-bit integers that edge NPUs execute efficiently.