There are several methods for calculating convolution, but the fastest method depends on the size of the input signal and the available hardware. Here are some of the commonly used methods:

1. Direct Convolution: This method calculates the convolution by performing a sliding dot product between the input signal and the filter. It is a straightforward method and easy to understand but can be slow for large input signals.

2. Fourier Transform: The Fourier transform can be used to convert the convolution operation into a multiplication operation in the frequency domain. This method is faster than direct convolution for large input signals, but it requires additional time and memory for the transformation.

3. Overlap-Add and Overlap-Save: These methods break the input signal into smaller segments and perform convolution on each segment. The output segments are then added or saved to produce the final output signal. These methods are efficient for large input signals and have a lower memory requirement compared to the Fourier transform method.

4. Winograd Convolution: This method is based on the Winograd algorithm, which is a fast algorithm for matrix multiplication. It is particularly efficient for small filter sizes and can be faster than the Fourier transform method for small input signals.

Overall, the choice of method depends on the specific application and hardware. For example, the Fourier transform method may be the fastest on a CPU, but the Winograd method may be faster on a GPU.