model compression for edge deployment

**Model Compression for Edge Deployment** is the **set of techniques to reduce neural network size and computational requirements** — enabling deployment of powerful models on resource-constrained edge devices (smartphones, IoT sensors, embedded controllers) with limited memory, compute, and power. **Compression Techniques** - **Pruning**: Remove redundant weights, neurons, or filters — structured (remove entire filters) or unstructured (individual weights). - **Quantization**: Reduce weight precision from 32-bit to 8-bit, 4-bit, or binary — smaller model, faster inference. - **Knowledge Distillation**: Train a small student model to mimic a large teacher model. - **Architecture Search**: Automatically design efficient architectures (NAS) for target hardware constraints. **Why It Matters** - **Edge AI**: Run ML models on fab equipment, sensors, and edge controllers without cloud connectivity. - **Latency**: On-device inference is milliseconds vs. 100ms+ for cloud inference — critical for real-time process control. - **Privacy**: On-device inference keeps data local — no data transmission to cloud servers. **Model Compression** is **fitting intelligence into tiny packages** — shrinking powerful models to run on resource-constrained edge devices.