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

Code Generation for Quantized 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 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.

Quantization for FPGA Target

Quantize Network for FPGA Deployment(Deep Learning HDL Toolbox)

This example shows how to quantize learnable parameters in the convolution layers of a neural network, and validate the quantized network.

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

Code Generation for Quantized Deep Learning Networks(MATLAB Coder)

Quantize and generate code for a pretrained convolutional neural network.

Code Generation for Quantized Deep Learning Network on Raspberry Pi(MATLAB Coder)

生成代码的深度学习的网络性能rms inference computations in 8-bit integers.