Using Unified APIs - 1.1 English

Vitis AI User Guide (UG1414)

Document ID

UG1414

Release Date

2020-03-23

Version

1.1 English

Vitis AI provides a C++ DpuRunner class with the following interfaces:

std::pair<uint32_t, int> execute_async(  const std::vector<TensorBuffer*>& input,  const std::vector<TensorBuffer*>& output);

Submit input tensors for execution, and output tensors to store results. The host pointer is passed via the TensorBuffer object. This function returns a job ID and the status of the function call.
```
int wait(int jobid, int timeout);
```
The job ID returned by execute_async is passed to wait() to block until the job is complete and the results are ready.
```
TensorFormat get_tensor_format()
```
Query the DpuRunner for the tensor format it expects.
Returns DpuRunner::TensorFormat::NCHW or DpuRunner::TensorFormat::NHWC
```
std::vector<Tensor*> get_input_tensors()
```
Query the DpuRunner for the shape and name of the input tensors it expects for its loaded AI model.
```
std::vector<Tensor*> get_output_tensors()
```
Query the DpuRunner for the shape and name of the output tensors it expects for its loaded AI model.

To create a DpuRunner object call the following:

DpuRunner::create_dpu_runner(const std::string& model_directory);

which returns

std::vector<std::unique_ptr<vitis::ai::DpuRunner>>

The input to create_dpu_runner is a model runtime directory generated by the AI compiler. The directory contains a meta.json that distinguishes each directory for each Vitis Runner, along with files needed by the Runner at runtime.