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

3D Max Pooling Forward Layer

The forward three-dimensional (3D) max pooling layer is a form of non-linear downsampling of an input tensor X R n 1 x n 2 x ... x n p . 3D max pooling partitions the input tensor data into 3D subtensors along dimensions k 1, k 2, and k 3, selects an element with the maximal numeric value in each subtensor, and transforms the input tensor to the output tensor Y by replacing each subtensor with its maximum element.

Problem Statement

Given:

The problem is to compute the value tensor Y = (y i 1 ...i p ) ∈ R l 1 x ... x l p using the downsampling technique.

The layer computes the value y i 1 ...i p as the maximum element in the subtensor. After the kernel is applied to the subtensor at position the index of the maximum T = (t i 1 ...i p ) is stored for use by the backward 3D max pooling layer:









where