RKNN2 V2.5.0 · GPU operator support and constraints

This RKNN2 V2.5.0 platform reference helps ArmSoM developers select and adapt models. It does not establish support on every ArmSoM board: verify the board SoC, actual Toolkit2, Runtime, NPU driver and BSP versions. The source specifies no universal Linux kernel version. No ArmSoM hardware validation was performed.

Source: the supplied Chinese and English RKNN2 Supported Operators and Constraints Reference V2.5.0, 374 and 384 pages respectively. Chinese follows the Chinese edition; English, Russian and German follow the English edition. Platform constraints remain separate. The English PDF itself contains Chinese I/O figures and output tables; their provenance is retained with editable localized tables. Source spellings such as ConvTranpose and the original LSTM/GRU equations are preserved; these equations are not corrections to SDK implementations.

Source pages: CN 367–367; EN 377–377.

Rockchip RKNN2

Operator reference series

RKNN2 V2.5.0 · RK3566/RK3568：Basics and convolution

RKNN2 V2.5.0 · RK3566/RK3568：Recurrent networks, arithmetic and pooling

RKNN2 V2.5.0 · RK3566/RK3568：Shapes, slicing and transpose

RKNN2 V2.5.0 · RK3566/RK3568：Activations, normalization and extensions

RKNN2 V2.5.0 · RK3588：Basics and convolution

RKNN2 V2.5.0 · RK3588：Recurrent networks, arithmetic and pooling

RKNN2 V2.5.0 · RK3588：Shapes, slicing and transpose

RKNN2 V2.5.0 · RK3588：Activations, normalization and extensions

RKNN2 V2.5.0 · RK3562：Basics and convolution

RKNN2 V2.5.0 · RK3562：Recurrent networks, arithmetic and pooling

RKNN2 V2.5.0 · RK3562：Shapes, slicing and transpose

RKNN2 V2.5.0 · RK3562：Activations, normalization and extensions

RKNN2 V2.5.0 · RV1103/RV1106：Basics and convolution

RKNN2 V2.5.0 · RV1103/RV1106：Recurrent networks, arithmetic and pooling

RKNN2 V2.5.0 · RV1103/RV1106：Shapes, slicing and transpose

RKNN2 V2.5.0 · RV1103/RV1106：Activations, normalization and extensions

RKNN2 V2.5.0 · RK3576/RV1126B：Basics and convolution

RKNN2 V2.5.0 · RK3576/RV1126B：Recurrent networks, arithmetic and pooling

RKNN2 V2.5.0 · RK3576/RV1126B：Shapes, slicing and transpose

RKNN2 V2.5.0 · RK3576/RV1126B：Activations, normalization and extensions

RKNN2 V2.5.0 · RK2118：Basics and convolution

RKNN2 V2.5.0 · RK2118：Recurrent networks, arithmetic and pooling

RKNN2 V2.5.0 · RK2118：Shapes, slicing and transpose

RKNN2 V2.5.0 · RK2118：Activations, normalization and extensions

RKNN2 V2.5.0 · RV1103B/RV1106B：Basics and convolution

RKNN2 V2.5.0 · RV1103B/RV1106B：Recurrent networks, arithmetic and pooling

RKNN2 V2.5.0 · RV1103B/RV1106B：Shapes, slicing and transpose

RKNN2 V2.5.0 · RV1103B/RV1106B：Activations, normalization and extensions

RKNN2 V2.5.0 · CPU operator support and constraints

RKNN2 V2.5.0 · GPU operator support and constraints

RKNN2 V2.5.0 · Model I/O, alignment and convolution limits

GPU OP Specifications

Note:

• RV1106/RV1103/RV1106B/RV1103B/RV1126B/RK2118 do not support GPU operators
• RK356X does not yet support GridSample, exGelu

GridSample

• Description: Interpolation sampling
• Specification Constraints: only FP16 is supported, the interpolation algorithm only supports bilinear, padding_mode only supports zeros

GRU

• Description: Gated Recurrent Unit
• Specification Constraints: only the case where hidden_size is 4-aligned is supported; the case where input_size and hidden_size are less than 4 is not supported

exGelu

• Description: Activation function
• Specification Constraints: Int8 implementation is currently supported