Stage 4: Model Optimization – Preparing AI for the Edge
A supervised model trained in the cloud is powerful, but it’s not yet ready to run on an edge device like a Digital View AI board with Pi CM5 and M.2 accelerator. The trained model is often too large, too power-hungry, or too slow.
Stage 4 is all about optimization: slimming the model down, translating it into accelerator-friendly form, and making sure it can deliver real-time performance at the edge without losing critical accuracy.
The Reason Optimization Is Needed
- Size: Training models can be hundreds of megabytes or even gigabytes. Edge devices need models measured in tens of megabytes.
- Speed: Inference in the field may require milliseconds of response time, not seconds.
- Efficiency: Edge deployments run within strict power and thermal limits.
- Compatibility: Each accelerator, Hailo, DeepX, Brainchip, Encharge, has its own runtime and compilation requirements.
Without optimization, even the most accurate model may fail to run effectively in the field.
Common Optimization Techniques
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Quantization
Reducing precision of numbers used in the model (e.g., 32-bit floats to 8-bit integers) to shrink size and increase speed with minimal accuracy loss. -
Pruning
Removing neurons or connections that contribute little to the final output, making the network lighter and faster. -
Knowledge Distillation
Training a smaller “student” model to mimic the behavior of the large “teacher” model, preserving performance while reducing size. -
Hardware-Specific Compilation
Converting the trained model into the format expected by the target accelerator:- HailoRT for Hailo-8 chips
- DeepX SDK for DeepX NPUs
- Brainchip Akida tools for event-based neural nets
- Encharge compilers for EN100 efficiency chips
Example: Optimizing for Safety Monitoring
Suppose supervised training (Stage 3) produced a vision model that detects “forklifts,” “workers,” and “idle areas.” The raw model is 500 MB and runs at 3 frames per second on a desktop GPU.
- After quantization, it shrinks to 50 MB.
- With pruning, latency drops by half.
- Compiled with HailoRT, it runs in real time on the Hailo-8 accelerator installed in the Digital View board.
Now the model is practical for deployment at a construction site — compact, fast, and field-ready.
Why It Matters
Model optimization is the bridge between training and deployment. It ensures that AI models are not just accurate, but also efficient, portable, and robust enough to run reliably at the edge.
This is the stage where your AI investment moves from the lab to something that can actually deliver results in the field.
Transition to Stage 5
With an optimized model in hand, the final step is Stage 5: Edge Deployment, running the model directly on Digital View AI boards for low-latency, real-world inference.
