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Accuracy

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Feature Scope

Data Types

**Table 1** Parameter data types
src Data Type	dst Data Type
f32	f32
f16	f16
bf16	bf16

Data Layout

The KDNN Sum operator supports the following data layout:

The data dimension can be 1D to 5D.
The N input tensors and output tensors must have the same dimension and data layout. For details, see the following table.

**Table 2** Mapping between each tensor dimension and parameter data layout
Tensor Dimension	src Data Layout	dst Data Layout
1D	dnnl_a	dnnl_a
2D	dnnl_ab	dnnl_ab
3D	dnnl_abc	dnnl_abc
3D	dnnl_acb	dnnl_acb
4D	dnnl_abcd	dnnl_abcd
4D	dnnl_acdb	dnnl_acdb
5D	dnnl_abcde	dnnl_abcde
5D	dnnl_acdeb	dnnl_acdeb

Parent topic: Sum