Quantization Workflow Prerequisites

This page describes the products required to quantize, simulate, and deploy deep learning networks using Deep Learning Toolbox Model Quantization Library. The prerequisites required depend on your selections at each stage of the quantization workflow.

Prerequisites for All Quantization Workflows

The following requirements apply to all stages of the quantization workflow.

Deep Learning Toolbox™
Deep Learning Toolbox Model Quantization Library

Supported Networks and Layers

The following links describe the networks and layers supported for each execution environment.

GPU — Supported Networks, Layers, and Classes (GPU Coder)
FPGA — Supported Networks, Layers, Boards, and Tools (Deep Learning HDL Toolbox)
CPU — Networks and Layers Supported for Code Generation (MATLAB Coder)
MATLAB — Networks and Layers Supported for Code Generation (MATLAB Coder)
Note
When the Execution Environment is set to MATLAB, only the layers for the Intel MKL-DNN deep learning library are supported.

Prerequisites for Calibration

The prerequisites for calibration depend on your selection of calibration environment.

Calibrate on host GPU (default) —
- Parallel Computing Toolbox™
- GPU Coder™ Interface for Deep Learning Libraries
- CUDA^® enabled NVIDIA^® GPU with compute capability 3.2 or higher.
Calibrate on host CPU —
- MATLAB^® Coder™ Interface for Deep Learning Libraries

On Windows^®, the MinGW C/C++ compiler is not supported. Use Microsoft Visual C++ 2019, Microsoft Visual C++ 2017, or Microsoft Visual C++ 2015.

On Linux^®, use a GCC C/C++ compiler.

For a list of supported compilers, see Supported and Compatible Compilers.

Prerequisites for Quantization

To quantize your network for simulation in MATLAB using the quantize function or the Export > Export Quantized Network option in the Deep Network Quantize app, no additional prerequisites are required.

Prerequisites for Validation

The following are required to validate your quantized network for deployment using the validate function or the Quantize and Validate button in the Deep Network Quantizer app.

Execution Environment	Prerequisites for Validation
GPU	Parallel Computing Toolbox GPU Coder Interface for Deep Learning Libraries CUDA enabled NVIDIA GPU with compute capability 6.1, 6.3 or higher. Setting Up the Prerequisite Products (GPU Coder)
FPGA	MATLAB Coder Interface for Deep Learning Libraries Deep Learning HDL Toolbox™ Deep Learning HDL Toolbox Support Package for Xilinx^® FPGA and SoC Devices Deep Learning HDL Toolbox Support Package for Intel^® FPGA and SoC Devices `hdlsetuptoolpath` (HDL Coder)
CPU	MATLAB Coder Interface for Deep Learning Libraries MATLAB Coder Embedded Coder^® Prerequisites for Deep Learning with MATLAB Coder (MATLAB Coder) Note Before validation, you must create a `raspi` object to establish connection to hardware.
MATLAB	N/A

For the FPGA execution environment, you can choose to validate your quantized network using simulation when you set the Simulate property of dlquantizer to 'on'. This option requires only Deep Learning HDL Toolbox.

For CPU and GPU deployment, 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.