Convolution Flavors

This project contains different types of implementations of convolution layer used in Convolutional Neural Networks.

Notations

N : Batch size

C : No of input channels

H : Input height

W : Input width

M : No of output channels

K : Kernel size

Description

The current implementations include

1. Image to Column - Standard convolution used in frameworks such as Caffe. This method requires an extra buffer space of HxWxKxKxC to store the im2col matrix. The matrix multiplication is done using SGEMM.

2. Image to Row - This is same as image to column method except the strided input patches are unrolled and stored in a row of the matrix. The extra buffer space requirement is same as im2col method.

3. Kernel to Row - This method is based on the trick that a KxK convolution can be computed using K.K 1x1 convolutions and then shifting and adding the resulting partial outputs. The extra buffer space required for this is MxHxW.

4. Kernel to Column - This is the counterpart of kn2row method.

Methods 3 and 4 are introduced in this research paper. Refer to it for more info.

5. Vectored Convolution - This implementation uses direct convolution without using any creation of patch matrix. It vectorizes the computation in C, M, W and K dimensions which can be changed to finetune the performance for different platforms.

6. 1D Winograd Convolution - This implementation is based on 1D Winograd convolution algorithm. This approach saves some costly multiplications at the expense of few more cheaper addtions.

Methods 5 and 6 are vectorized as per this paper from Intel which is on Deep learning accelerator using OpenCL on Arria10 FPGA.

Refer to Fast Algorithms for Convolutional Neural Networks for details on Winograd appraoch to realize convolution layers.