A high-performance general-purpose compute library

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Convolution

A convolution is a common operation between a source array, a, and a filter (or kernel) array b. The answer to the convolution is the same as computing the coefficients in polynomial multiplication, if a and b are the coefficients.

Another way to think about it is that the filter kernel is centered on each pixel in a, and the output for that pixel or data point is the sum of the products.

Depending on the size of the signal and the filter, any one of the following batch mode convolutions take place.

- No Batch - Single filter applied to single input.
- Filter is Batched - Many filters applied on same input
- Signal is Batched - Single filter applied to a set of inputs.
- Identical Batches - A set of filters applied onto to a set of inputs in one-to-one correspondence.
- Non overlapping Batches - All batched filters are applied to all batched signals. The batch axis of Signal and Filter should not be the same.

Note
  All non-overlapping(interleaved) convolutions default to frequency domain AF_CONV_FREQ irrespective of the provided convolution mode argument.