Developer Guide for Intel® Data Analytics Acceleration Library 2019 Update 5

2D Locally-Connected Forward Layer

The forward two-dimensional (2D) locally-connected layer computes the value tensor Y by applying a set of nKernels 2D kernels K of size m 1 x m 2 to the input argument 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 + a < n 1, j + b < n 2, and r is the kernel index.

A set of kernels is specific to the selected dimensions of the input argument x.

See [GregorLecun2010] for additional details of the two-dimensional locally-connected layer.

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: