How do you implement convolution in Cuda?

The simplest approach to implement convolution in CUDA is to load a block of the image into a shared memory array, do a point-wise multiplication of a filter-size portion of the block, and then write this sum into the output image in device memory. Each thread block processes one block in the image.

How do you vectorize convolution?

Strategy to vectorize convolution

1. Convert all kernels/ filters to rows and get a kernel matrix.
2. Split your input (image) into slices for convolution then convert to columns and get an input matrix. You can append other inputs (images) to form a mini-batch.
3. multiply input matrix with the kernels matrix.

What is implicit GEMM?

Implicit GEMM is the formulation of a convolution operation as a GEMM (generalized matrix-matrix product). Convolution takes an activation tensor and applies a sliding filter on it to produce an output tensor.

How does convolution work in Python?

Convolution is an operation that is performed on an image to extract features from it applying a smaller tensor called a kernel like a sliding window over the image. Depending on the values in the convolutional kernel, we can pick up specific patterns from the image.

Does cuDNN use FFT?

cuDNN also includes a Winograd transform method and an FFT-based method.

What is Winograd convolution?

Winograd- and FFT-based convolution are two efficient convolution algorithms targeting high-performance infer- ence. Their efficiency comes from the reduction of the num- ber of multiplication operations due to linear and Fourier transforms.

How do you implement CNN from scratch?

The major steps involved are as follows:

1. Reading the input image.
2. Preparing filters.
3. Conv layer: Convolving each filter with the input image.
4. ReLU layer: Applying ReLU activation function on the feature maps (output of conv layer).
5. Max Pooling layer: Applying the pooling operation on the output of ReLU layer.

Does cuDNN use Winograd?

Owing to its significant performance benefits, Winograd convolution has quickly gained its popularity and has been supported by modern deep learning libraries such as Nvidia cuDNN and Intel(R) MKL-DNN.

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