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 target hardware associated with each execution environment determines the supported networks and layers for that environment.

GPU — Supports networks and layers that support code generation for the CUDA^® Deep Neural Network library (cuDNN). For a list of these networks and layers, see Supported Networks, Layers, and Classes (GPU Coder)
FPGA — Supports networks and layers that support code generation for Xilinx^® and Intel^® FPGA and SoC devices. For a list of these networks and layers, see Supported Networks, Layers, Boards, and Tools
CPU — Supports networks and layers that support code generation for the ARM^® Compute Library. For a list of these networks and layers, see Networks and Layers Supported for Code Generation (MATLAB Coder)
MATLAB — Supports networks and layers that support code generation for the Intel MKL-DNN Compute Library. For a list of these networks and layers, see Networks and Layers Supported for Code Generation (MATLAB Coder)

Prerequisites for Calibration

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

Calibrate on host GPU —
- Parallel Computing Toolbox™
- GPU Coder™ Interface for Deep Learning
- CUDA enabled NVIDIA^® GPU with compute capability 3.5 or higher.
- Supported C/C++ Compiler
  - On Windows^®, the MinGW C/C++ compiler is not supported. Use Microsoft Visual C++ 2022, Microsoft Visual C++ 2019, or Microsoft Visual C++ 2017.
  - On Linux^®, use a GCC C/C++ compiler.
    For a list of supported compilers, see Supported and Compatible Compilers.
Calibrate on host CPU —
- MATLAB^® Coder™ Interface for Deep Learning or GPU Coder Interface for Deep Learning

Prerequisites for Quantization

The following are required to quantize your network for simulation in MATLAB using the quantize function or the Export > Export Quantized Network option in the Deep Network Quantizer app.

When you use the MATLAB Execution Environment, a Fixed-Point Designer™ license is required for quantization.
When you use the CPU Execution Environment, the quantization step is not supported.
When you use any other Execution Environment for quantization, 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 Validate button in the Deep Network Quantizer app.

Execution Environment	Prerequisites for Validation
GPU	Parallel Computing Toolbox GPU Coder Interface for Deep Learning CUDA enabled NVIDIA GPU with compute capability 6.1, 6.3 or higher. Setting Up the Prerequisite Products (GPU Coder)
FPGA	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 MATLAB Coder Embedded Coder^® ARM Compute Library. For more information, see Prerequisites for Deep Learning with MATLAB Coder (MATLAB Coder)
MATLAB	Fixed-Point Designer

For CPU and GPU deployment, the software generates code for a 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.

Validation on target hardware for CPU, FPGA, and GPU execution environments is not supported in MATLAB Online™. For FPGA and GPU execution environments, validation can be performed through emulation on the MATLAB Online host. GPU validation can also be performed if GPU support has been added to your MATLAB Online Server™ cluster. For more information on GPU support for MATLAB Online, see Configure GPU Support in MATLAB Online Server (MATLAB Online Server).