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.