Supported Deep Learning Toolbox Blocks with CMSIS Library for ARM Cortex-M Processors

Deep learning layer blocks that can be used with the Embedded Coder® Support Package for ARM® Cortex®-M Processors require specific conditions to allow code replacement with the common microcontroller software interface standard (CMSIS) library.

You use this code replacement when generating C code from a model. The CMSIS library support these Deep Learning Toolbox™ layer blocks only when you set specific properties, as indicated in the following table.

Deep Learning Layer Blocks

Deep Learning Layer Blocks

Deep Learning Toolbox Blocks Supported Input Data TypesParameter SpecificationsWrappers calling CMSIS function
Convolution 2D Layer (Deep Learning Toolbox)

  • fixdt(1,8,*)

  • Weights must be fixdt(1,8,*).

  • Bias must be fixdt(1,32,*)

  • Output data type must be fixdt(1,8,*) or fixdt(1,32,*).

Note

  • Code replacement occurs only when the Layer parameter supports convolution2dLayer (Deep Learning Toolbox) objects that have DilationFactor set to the default value [1 1].

  • Padding: Supports both symmetric and asymmetric padding. Asymmetric padding allows different padding sizes for top, bottom, left, and right edges.

  • The PaddingValue property of convolution2dLayer (Deep Learning Toolbox) object must be a scalar and equal to 0.

  • arm_convolve_wrapper_s8

Fully Connected Layer (Deep Learning Toolbox)

  • fixdt(1,8,*)

  • Weights must be fixdt(1,8,*).

  • Bias must be fixdt(1,32,*)

  • Output data type must be fixdt(1,8,*).

  • arm_fully_connected_s8

See Also

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