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MMALIB User Guide
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This module consists of kernels to implement the core computations occurring in the context of convolutional neural networks.
Sub Modules | |
MMALIB_CNN_convolveBias_row_ixX_ixX_oxX | |
Kernel for computing dense CNN convolution with row based processing. | |
MMALIB_CNN_convolve_col_smallNo_highPrecision | |
NOTE: This API is now a wrapper to MMALIB_CNN_convolve_col_smallNo_highPrecision_pointwisePost with the lutValues input argument set to NULL. It is recommended to call MMALIB_CNN_convolve_col_smallNo_highPrecision_pointwisePost directly. | |
MMALIB_CNN_convolve_col_smallNo_highPrecision_pointwisePost | |
Kernel for computing CNN-style 2D convolution using column major data ordering on the input and output feature maps. This approach computes more quickly if filter grouping is chosen such that Ni=No=1, or if filter grouping is chosen such that NiFrFc < MMA_SIZE, otherwise use regular convolution method MMALIB_CNN_convolve_row_ixX_ixX_oxX. This kernel is also referred to as depth-wise convolution. | |
MMALIB_CNN_convolve_col_smallNo_ixX_ixX_oxX | |
Kernel for computing CNN-style 2D convolution using column major data ordering on the input and output feature maps. This approach computes more quickly if filter grouping is chosen such that Ni=No=1, or if filter grouping is chosen such that NiFrFc < MMA_SIZE, otherwise use regular convolution method MMALIB_CNN_convolve_row_ixX_ixX_oxX. This kernel is also referred to as depth-wise convolution. | |
MMALIB_CNN_deconvolve_row_ixX_ixX_oxX | |
Kernel for computing dense CNN deconvolution with row-based processing and matrix-matrix multiplication. | |
MMALIB_CNN_fullyConnectedBias_ixX_ixX_oxX | |
Kernel provides compute functionality of Fully Connected Layer: \( Y^T = X^T \times H^T + B^T\). | |
MMALIB_CNN_tensor_convert_ixX_oxX | |
Kernel for converting tensors of various datatypes and formats. | |