# Quantization and Pruning

Use Deep Learning Toolbox™ together with the Deep Learning Toolbox Model Quantization Library support package to reduce the memory footprint and computational requirements of a deep neural network by:

Quantizing the weights, biases, and activations of layers to reduced precision scaled integer data types. You can then generate C/C++, CUDA

^{®}, or HDL code from this quantized network.For C/C++ and CUDA code generation, the software generates code for a convolutional deep neural network by quantizing the weights, biases, and activations of the convolution layers to 8-bit scaled integer data types. The quantization is performed by providing the calibration result file produced by the

`calibrate`

function to the`codegen`

(MATLAB Coder) command.Code generation does not support quantized deep neural networks produced by the

`quantize`

function.Pruning filters from convolution layers by using first-order Taylor approximation. You can then generate C/C++ or CUDA code from this pruned network.

## Functions

## Apps

Deep Network Quantizer | Quantize a deep neural network to 8-bit scaled integer data types |

## Topics

### Deep Learning Quantization

**Quantization of Deep Neural Networks**

Understand effects of quantization and how to visualize dynamic ranges of network convolution layers.**Quantization Workflow Prerequisites**

Products required for the quantization of deep learning networks.

### Quantization for GPU Target

**Generate INT8 Code for Deep Learning Networks (GPU Coder)**

Quantize and generate code for a pretrained convolutional neural network.**Quantize Residual Network Trained for Image Classification and Generate CUDA Code**

This example shows how to quantize the learnable parameters in the convolution layers of a deep learning neural network that has residual connections and has been trained for image classification with CIFAR-10 data.**Quantize Layers in Object Detectors and Generate CUDA Code**

This example shows how to generate CUDA® code for an SSD vehicle detector and a YOLO v2 vehicle detector that performs inference computations in 8-bit integers for the convolutional layers.

### Quantization for FPGA Target

**Deploy INT8 Network to FPGA (Deep Learning HDL Toolbox)**

Reduce the memory footprint of a deep neural network by quantizing the weights, biases, and activations of convolution layers to 8-bit scaled integer data types.**Classify Images on an FPGA Using a Quantized DAG Network (Deep Learning HDL Toolbox)**

In this example, you use Deep Learning HDL Toolbox™ to deploy a quantized deep convolutional neural network and classify an image.**Classify Images on FPGA by Using Quantized GoogLeNet Network (Deep Learning HDL Toolbox)**

This example show how to use the Deep Learning HDL Toolbox™ to deploy a quantized GoogleNet network to classify an image.

### Quantization for CPU Target

**Generate int8 Code for Deep Learning Networks (MATLAB Coder)**

Quantize and generate code for a pretrained convolutional neural network.**Generate INT8 Code for Deep Learning Network on Raspberry Pi (MATLAB Coder)**

Generate code for deep learning network that performs inference computations in 8-bit integers.

### Pruning

**Parameter Pruning and Quantization of Image Classification Network**

Use parameter pruning and quantization to reduce network size.**Prune Image Classification Network Using Taylor Scores**

This example shows how to reduce the size of a deep neural network using Taylor pruning.**Prune Filters in a Detection Network Using Taylor Scores**

This example shows how to reduce network size and increase inference speed by pruning convolutional filters in a you only look once (YOLO) v3 object detection network.