Knowledge Distillation for Edge is the training of a small, efficient student model to mimic a large, accurate teacher model — specifically optimized for deployment on edge devices with strict memory, compute, and latency constraints.

Edge-Specific Distillation

• Hardware-Aware: Design the student architecture for target hardware (ARM, RISC-V, MCU, NPU).
• Latency-Constrained: Student architecture is chosen to meet latency requirements on target hardware.
• Multi-Teacher: Distill from multiple teacher models (ensemble) into a single edge-friendly student.
• Feature Distillation: Match intermediate representations (not just outputs) for richer knowledge transfer.

Why It Matters

• Accuracy Retention: Distilled students retain 90-99% of teacher accuracy at 10-100× smaller size.
• Deployment: A 50MB teacher → 5MB student can run on embedded processors in fab equipment.
• Real-Time: Distilled models enable real-time inference on edge devices for process monitoring and control.

Distillation for Edge is compressing expert knowledge into a tiny model — transferring a large model's intelligence into an edge-deployable student.