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### Description - Update EP ordering and distinguish between MS-maintained and community-maintained EPs - Update CUDA version table for v1.13 - Staged preview: https://faxu.github.io/onnxruntime/docs/execution-providers/
93 lines
2.1 KiB
Markdown
93 lines
2.1 KiB
Markdown
---
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title: Rockchip - RKNPU
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description: Instructions to execute ONNX Runtime on Rockchip NPUs with the RKNPU execution provider
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grand_parent: Execution Providers
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parent: Community-maintained
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nav_order: 5
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redirect_from: /docs/reference/execution-providers/RKNPU-ExecutionProvider
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---
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# RKNPU Execution Provider
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*PREVIEW*
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RKNPU DDK is an advanced interface to access Rockchip NPU. The RKNPU Execution Provider enables deep learning inference on Rockchip NPU via RKNPU DDK.
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## Contents
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{: .no_toc }
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* TOC placeholder
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{:toc}
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## Build
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For build instructions, please see the [BUILD page](../../build/eps.md#rknpu).
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## Usage
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**C/C++**
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To use RKNPU as an execution provider for inferencing, please register it as below.
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```
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Ort::Env env = Ort::Env{ORT_LOGGING_LEVEL_ERROR, "Default"};
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Ort::SessionOptions sf;
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Ort::ThrowOnError(OrtSessionOptionsAppendExecutionProvider_RKNPU(sf));
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Ort::Session session(env, model_path, sf);
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```
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The C API details are [here](../../get-started/with-c.md).
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## Support Coverage
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### Supported Platform
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{: .no_toc }
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* RK1808 Linux
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*Note: RK3399Pro platform is not supported.*
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### Supported Operators
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{: .no_toc }
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The table below shows the ONNX Ops supported using the RKNPU Execution Provider and the mapping between ONNX Ops and RKNPU Ops.
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| **ONNX Ops** | **RKNPU Ops** |
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| --- | --- |
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| Add | ADD |
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| Mul | MULTIPLY |
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| Conv | CONV2D |
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| QLinearConv | CONV2D |
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| Gemm | FULLCONNECT |
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| Softmax | SOFTMAX |
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| AveragePool | POOL |
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| GlobalAveragePool | POOL |
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| MaxPool | POOL |
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| GlobalMaxPool | POOL |
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| LeakyRelu | LEAKY_RELU |
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| Concat | CONCAT |
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| BatchNormalization | BATCH_NORM |
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| Reshape | RESHAPE |
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| Flatten | RESHAPE |
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| Squeeze | RESHAPE |
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| Unsqueeze | RESHAPE |
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| Transpose | PERMUTE |
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| Relu | RELU |
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| Sub | SUBTRACT |
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| Clip(0~6)| RELU6 |
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| DequantizeLinear | DATACONVERT |
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| Clip | CLIP |
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### Supported Models
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{: .no_toc }
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The following models from the ONNX model zoo are supported using the RKNPU Execution Provider
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**Image Classification**
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- squeezenet
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- mobilenetv2-1.0
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- resnet50v1
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- resnet50v2
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- inception_v2
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**Object Detection**
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- ssd
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- yolov3
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