knowledge distillation for edge

**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.