QNN Execution Provider

The QNN Execution Provider for ONNX Runtime enables hardware accelerated execution on Qualcomm chipsets. It uses the Qualcomm AI Engine Direct SDK (QNN SDK) to construct a QNN graph from an ONNX model which can be executed by a supported accelerator backend library.

Contents

Install Pre-requisites

Download the Qualcomm AI Engine Direct SDK (QNN SDK) from https://qpm.qualcomm.com/main/tools/details/qualcomm_ai_engine_direct

QNN Version Requirements

ONNX Runtime QNN Execution Provider has been built and tested with QNN 2.10.x and Qualcomm SC8280, SM8350 SOC’s

Build

For build instructions, please see the BUILD page. prebuilt NuGet package

Configuration Options

The QNN Execution Provider supports a number of configuration options. The provider_option_keys, provider_options_values enable different options for the application. Each provider_options_keys accepts values as shown below:

provider_options_values for provider_options_keys = "backend_path" Description
‘libQnnCpu.so’ or ‘QnnCpu.dll’ Enable CPU backend. Useful for integration testing. CPU backend is a reference implementation of QNN operators
‘libQnnHtp.so’ or ‘QnnHtp.dll’ Enable Htp backend. Offloads compute to NPU.
provider_options_values for provider_options_keys = "profiling_level" Description
‘off’  
‘basic’  
‘detailed’  
provider_options_values for provider_options_keys = "rpc_control_latency" Description
microseconds (string) allows client to set up RPC control latency in microseconds
provider_options_values for provider_options_keys = "htp_performance_mode" Description
‘burst’  
‘balanced’  
‘default’  
‘high_performance’  
‘high_power_saver’  
‘low_balanced’  
‘low_power_saver’  
‘power_saver’  
‘sustained_high_performance’  
provider_options_values for provider_options_keys = "qnn_context_cache_enable" Description
‘0’ disabled (default)
‘1’ enable qnn context cache. write out prepared Htp Context Binary to disk to save initialization costs.
provider_options_values for provider_options_keys = "qnn_context_cache_path" Description
‘/path/to/context/cache’ string path to context cache binary
provider_options_values for provider_options_keys = "qnn_context_embed_mode" Description
‘0’ generate the QNN context binary into separate file, set path in ONNX file specified by qnn_context_cache_path.
‘1’ generate the QNN context binary into the ONNX file specified by qnn_context_cache_path (default).
provider_options_values for provider_options_keys = "qnn_context_priority" Description
‘low’  
‘normal’ default.
‘normal_high’  
‘high’  
provider_options_values for provider_options_keys = "htp_graph_finalization_optimization_mode" Description
‘0’ default.
‘1’ faster preparation time, less optimal graph.
‘2’ longer preparation time, more optimal graph.
‘3’ longest preparation time, most likely even more optimal graph.

Usage

C++

C API details are here.

Ort::Env env = Ort::Env{ORT_LOGGING_LEVEL_ERROR, "Default"};
std::unordered_map<std::string, std::string> qnn_options;
qnn_options["backend_path"] = "QnnHtp.dll";
Ort::SessionOptions session_options;
session_options.AppendExecutionProvider("QNN", qnn_options);
Ort::Session session(env, model_path, session_options);

Python 

import onnxruntime as ort
# Create a session with QNN EP using HTP (NPU) backend.
sess = ort.InferenceSession(model_path, providers=['QNNExecutionProvider'], provider_options=[{'backend_path':'QnnHtp.dll'}])`

Inference example

Image classification with Mobilenetv2 in CPP using QNN Execution Provider with QNN CPU & HTP Backend