Developer Guide for Intel® Data Analytics Acceleration Library 2017 Update 3

2D Convolution Forward Layer

The forward two-dimensional (2D) convolution layer computes the tensor Y of values by applying a set of nKernels 2D kernels K of size m 3 x m 4 to the input tensor X. The library supports four-dimensional input tensors XR n 1 x n 2 x n 3 x n 4 . Therefore, the following formula applies:



where i + u < n 3, j + v < n 4, and r is the kernel index.

Problem Statement

Without loss of generality, let's assume that convolution kernels are applied to the last two dimensions.

Given:

For the above tensors:

The problem is to compute the four-dimensional tensor of values Y R n 1 x nKernels x l 3 x l 4 such that:











where: