Deep learning library for simulation
Deep learning library to use during simulation
Since R2020b
Model Configuration Pane: Simulation Target
Description
The Deep learning library parameter specifies the deep
learning library to use during simulation. To enable the Deep learning
library parameter, set Language to
C++
. When the Language is
C
, the Deep learning library parameter
is hidden and the value of this parameter is None
.
Dependencies
When Language is set to
C++
and GPU acceleration is disabled:Default value for Target library is
MKL-DNN
.Option
None
orMKL-DNN
is available for Target library.
When GPU acceleration on the Simulation Target pane is enabled (requires a GPU Coder™ license):
Default value for Target library is
cuDNN
.Option
None
,cuDNN
orTensorRT
is available for Target library.
If Target library is previously set to
None
, the value for Target library stays asNone
when GPU acceleration is enabled or disabled.
Settings
None
| MKL-DNN
| cuDNN
| TensorRT
Default:
None
if GPU acceleration is off and Language isC
.MKL-DNN
if GPU acceleration is off and Language isC++
.cuDNN
if GPU acceleration is on.
None
(since R2025a)Simulates the model that is supported for generic C/C++ and plain CUDA workflows. For more information on networks and layers supported for code generation, see and Networks and Layers Supported for Code Generation (MATLAB Coder).
MKL-DNN
Simulates the model using the Intel® Math Kernel Library for Deep Neural Networks (Intel MKL-DNN).
cuDNN
Simulate the model using the CUDA® Deep Neural Network library (cuDNN).
TensorRT
Simulate the model using the NVIDIA® TensorRT high performance deep learning inference optimizer and run-time library.
Recommended Settings
Application | Setting |
---|---|
Debugging | No impact |
Traceability | No impact |
Efficiency | No impact |
Safety precaution | No impact |
Programmatic Use
Parameter:
SimDLTargetLibrary |
Type: character vector |
Value:'None' |
'MKL-DNN' | 'cuDNN' |
'TensorRT' |
Default:
'None' for language C | 'MKL-DNN' for
language C++ |