# Use Space-to-depth transformation where possible

- [Convolution](https://docs.qualcomm.com/doc/80-63442-10/topic/htp_guidelines_space_to_depth.html#convolution)
- [Elementwise Operations](https://docs.qualcomm.com/doc/80-63442-10/topic/htp_guidelines_space_to_depth.html#elementwise-operations)
- [Reshaping Data](https://docs.qualcomm.com/doc/80-63442-10/topic/htp_guidelines_space_to_depth.html#reshaping-data)

## [Convolution](https://docs.qualcomm.com/doc/80-63442-10/topic/htp_guidelines_space_to_depth.html#id1)

If a low input channel activation goes through a space-to-depth transformation, we can make
a convolution equivalent by doing the equivalent space-to-depth transformation in the weights.
For example, if we have original activations that are 1024x1024x8, we can run them through a
space-to-depth transformation with 2x2 size.  The resulting activations would be 512x512x32,
which matches better with the hardware functionality.  We would then need to take the weights
and modify them to match the new data arrangement.  If the original weights were 3x3x8x8
(h\*w\*n\_I\*n\_o), we could do a similar space-to-depth transformation, replicating the weights
four times to match the new location.  We need to round up to handle all overlap, so we would
end up in this case with weights that were sized 2x2x32x32.

![../../_static/resources/htp_guidelines_fig_9.png](data:image/png;base64,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)

**Transformations to convolution to increase channel use**

When several convolutions are sequential, the back-to-back depth-to-space and space-to-depth
can be eliminated.  Also, space-to-depth followed by convolution is a way to implement convolution
with stride, and convolution followed by depth-to-space is a way to implement “transpose convolution
with stride”.
For some models, the space-to-depth and smaller filter size may be able to be modified in the
original model and trained with the structure in-place.  Otherwise, the description above should
yield exact results to the original strategy.

## [Elementwise Operations](https://docs.qualcomm.com/doc/80-63442-10/topic/htp_guidelines_space_to_depth.html#id2)

If both inputs of an elementwise operator have had the same space-to-depth operation applied to
them, they can be operated on with the same operator.

## [Reshaping Data](https://docs.qualcomm.com/doc/80-63442-10/topic/htp_guidelines_space_to_depth.html#id3)

It is common to see low channel convolution near the output of some networks, particularly ones
that do image processing.  The data arrangement is the same for an image with low channels, or an
image that has had a width-to-depth transformation.  Consider using a width-to-depth transformation
to increase the channels and then treating the output as the desired original low-channel and wider
image.  For example, a 1024x1024x3 could be computed as 1024x256x12, and then the output used as
1024x1024x3.

Last Published: Jun 04, 2026

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