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* Initial update of readme * Readme updates * Review of consolidated README (#3930) * Proposed updates for readme (#3953) I found some of the information was duplicated within the doc, so attempted to streamline * Fix links * More updates - fix build instructions - nodejs doc reorganization - roadmap update - version fixes * Update ORT Server build instructions * More doc cleanup * fix python dev notes name * Update nodejs and some links * sync eigen version back to master * Minor fixes * add nodsjs to sample table of content * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * address PR feedback * address PR feedback * nodejs build instruction * Update Java instructions to include gradle * Roadmap refresh Reformat some data, fix link, minor rewording * Clarify Visual C++ runtime req Co-authored-by: Nat Kershaw (MSFT) <nakersha@microsoft.com> Co-authored-by: Prasanth Pulavarthi <prasantp@microsoft.com> Co-authored-by: manashgoswami <magoswam@microsoft.com>
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Markdown
77 lines
No EOL
2 KiB
Markdown
# RKNPU Execution Provider (preview)
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RKNPU DDK is an advanced interface to access Rockchip NPU. RKNPU Execution Provider enables deep learning inference on Rockchip NPU via RKNPU DDK.
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## Supported platforms
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* RK1808 Linux
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*Note: RK3399Pro platform is not supported.*
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## Build
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For build instructions, please see the [BUILD page](../../BUILD.md#RKNPU).
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## Usage
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### C/C++
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To use RKNPU as execution provider for inferencing, please register it as below.
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```
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string log_id = "Foo";
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auto logging_manager = std::make_unique<LoggingManager>
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(std::unique_ptr<ISink>{new CLogSink{}},
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static_cast<Severity>(lm_info.default_warning_level),
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false,
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LoggingManager::InstanceType::Default,
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&log_id)
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Environment::Create(std::move(logging_manager), env)
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InferenceSession session_object{so,env};
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session_object.RegisterExecutionProvider(std::make_unique<::onnxruntime::RknpuExecutionProvider>());
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status = session_object.Load(model_file_name);
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```
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The C API details are [here](../C_API.md#c-api).
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## Supported Operators
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The table below shows the ONNX Ops supported using 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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Below Models are supported from ONNX open model zoo using 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 |