Developer Guide

Contents

One-Dimensional Average Pooling Forward Layer

The forward one-dimensional (1D) average pooling layer is a form of non-linear downsampling of an input tensor
X
= (
x
(1)
...
x
(
p
)
) of size
n
1
x
n
2
x ... x
n
p
. 1D average pooling partitions the input tensor data into 1D subtensors by one dimension
k
, computes the average value of elements in each subtensor, and transforms the input tensor to the output tensor
Y
= (
y
(1)
...
y
(
p
)
) of size
m
1
x
m
2
x ... x
m
p
by replacing each subtensor with one element, the average of the subtensor:
Here
f
k
is the kernel size of the pooled subtensor for the dimension
k
and
s
k
is the stride, that is, an interval on which each subtensor is selected;
p
k
is the padding for the dimension
k
.
The size
m
1
x
m
2
x ... x
m
p
of the output tensor of the forward 1D pooling layer
Y
is
f
k
cannot be greater than
n
k
.

Problem Statement

To perform average pooling, the problem is to compute the average for each subtensor and apply the transform to the input data:

Product and Performance Information

1

Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

Notice revision #20110804