libQnnHtp.so: Library used for communication between the Arm CPU and the Hexagon Tensor Processor.
libQnnHtpPrepare.so: Hexagon Tensor Processor library running on the CPU side. This library prepares the graph and optimizes the execution graph at runtime.
libQnnHtpV68Stub.so: Hexagon Tensor Processor proxy library on the CPU side, communicating with the Skel library loaded on the Hexagon Tensor Processor.
libQnnHtpV68Skel.so: Hexagon Tensor Processor native library with optimized kernel/operator implementation for the Hexagon Tensor Processor.
|
Note
Select the appropriate libraries based on the Hexagon Tensor Processor version of the Qualcomm Linux development kit. The versions are as follows:
- QCS6490/QCS5430: Hexagon Tensor Processor v68
- QCS9075: Hexagon Tensor Processor v73
- QCS8275: Hexagon Tensor Processor v75
### LiteRT external delegate interface
To run LiteRT models using an external delegate interface, the application must load the `libQnnTFLiteDelegate.so` QNN delegate library. The C/C++ application and the `libQnnTFLiteDelegate.so` delegate library have no dependency on each other. Therefore, if the delegate changes, you don’t have to recompile the application.
To use the C++ API and run inference with LiteRT on Linux, see [Run inference using C++](https://ai.google.dev/edge/litert/inference#run-c).
The QNN delegate provides acceleration on the Hexagon Tensor Processor, GPU, and CPU. To customize where and how to run models using the QNN delegate with the external delegate interface, you must provide more external delegate options. For instructions, see [External delegate options for QNN delegate](https://docs.qualcomm.com/doc/80-70018-54/topic/sample-applications.html#external-delegate-options-for-qnn-delegate).
## Next steps
- [Get started](https://docs.qualcomm.com/doc/80-70018-54/topic/getting-started.html#getting-started)
- [Run LiteRT sample applications](https://docs.qualcomm.com/doc/80-70018-54/topic/sample-applications.html#run-litert-sample-apps)
Last Published: Apr 07, 2025
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