Core AI Model Optimization: Quantization & Layer Metrics
Core AI Model Optimization: Quantization & Layer Metrics
Deploying machine learning models to mobile devices requires balancing model size and inference accuracy. Standard deep learning models use 16-bit or 32-bit floating-point weights, which can result in large bundle sizes and high memory usage. This makes running them on mobile hardware challenging.
To reduce model footprint, the Core AI compiler includes coreai-opt, a command-line optimization tool. It supports per-layer quantization and palettization, allowing you to compress weights down to 8-bit, 4-bit, or custom representation widths while monitoring accuracy degradation.
Quantizing Models with coreai-opt
The coreai-opt utility applies optimization configurations to your compiled .coreai model bundle.
# Convert a Float16 model to a compressed 8-bit quantized model
# Developer Thoughts: We specify the calibration dataset to calculate
# optimal dynamic scaling factors for activation tensors.
coreai-opt --input MyModel.coreai --output MyModel_q8.coreai --quantize-linear 8 --calibration-data calibration_set.binEvaluating Layer Metrics Post-Quantization
Quantization changes continuous floating-point weights into discrete steps. This can introduce rounding errors that degrade model accuracy. To find which layers are most sensitive to compression, you run an accuracy evaluation script using the Core AI Python SDK.
import coreai as ca
# Load the baseline and optimized models
baseline_model = ca.Model("MyModel.coreai")
optimized_model = ca.Model("MyModel_q8.coreai")
# Analyze differences across layers
# Developer Thoughts: Evaluating accuracy per-layer lets you identify
# layers that suffer high degradation (low signal-to-noise ratio).
# You can then choose to keep those sensitive layers at Float16.
comparison = ca.Diagnostics.compare_layers(baseline_model, optimized_model)
for layer in comparison.layers:
if layer.rmse_error > 0.05:
print(f"Layer {layer.name} has high quantization error: {layer.rmse_error:.4f}")If a specific layer (such as the initial convolution block or attention projection matrix) shows high error metrics, you can configure coreai-opt to exclude that layer from quantization, keeping it in high-precision Float16 while compressing the rest of the model.
Summary
Optimizing Core AI models using coreai-opt helps reduce app size and memory usage. By applying per-layer quantization and measuring accuracy metrics, you can balance model compression against performance degradation. Keep in mind that quantization parameters should be validated against a realistic calibration dataset to prevent accuracy regressions on production inputs.