Deep Learning Toolbox Model Quantization Library enables quantization and compression of your deep learning models to reduce the memory footprint and computational requirements of your deep neural network.
Quantization to INT8 is supported for CPUs, FPGAs, and NVIDIA GPUs, for supported layers. The library enables you to collect layer level data on the weights, activations, and intermediate computations. Using this data, the library quantizes your model and provides metrics to validate the accuracy of the quantized network against the single precision baseline. The iterative workflow allows you to optimize the quantization strategy.
The library also supports pruning which reduces network size by removing network elements that have the smallest impact on inference accuracy.
An example of Quantization Aware Training (QAT) with MobileNet-v2 is described at this GitHub link. The full GitHub repository can be found at this link.
Please refer to the documentation here: https://www.mathworks.com/help/deeplearning/quantization.html
Quantization Workflow Prerequisites can be found here:
If you have download or installation problems, please contact Technical Support - www.mathworks.com/contact_ts
MATLAB Release Compatibility
Created with R2020a
Compatible with R2020a to R2023b
Platform CompatibilityWindows macOS (Apple silicon) macOS (Intel) Linux
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