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™
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)
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 —
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
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, 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|
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
function to the
codegen (MATLAB Coder) command.
Code generation does not support quantized deep neural networks produced by the
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).