QNN DSP Predict

R2026b

Predict responses of QNN model for DSP backend

Since R2026a

• 

QNN DSP Predict block

To add a block to a model, double-click the canvas and start typing the block name. Then, select the block from the list.

Libraries:
Embedded Coder Support Package for Qualcomm Hexagon Processors / QNN

Description

The QNN DSP Predict block predicts responses of a deep learning network represented as a QNN model for the DSP backend of Qualcomm^® AI Direct Engine based on the given input data.

You use the QNN DSP Predict block to select a QNN model as a compiled shared object (.so) for running on a x86-based host. For the target, you can select a compiled shared object (.so) that is optimized to run on DSP backend. The Simulink^® model with this block can be deployed to Qualcomm Android Board that is available as supported board in the support package.

The code generated using this block can be deployed to one of these boards that are available under the Hardware board parameter in Configuration Parameters:

• Qualcomm Android Board

• Qualcomm Linux Board

The block also provides the option to dequantize outputs to single-precision, if required.

Ports

Input

Port_1 — Input signal to predict response

n–D array

The input tensor to use for inference with the selected QNN model, represented as an n-D array, in accordance with the Input layer size parameter of the QNN model.

The QNN DSP Predict block supports a multiple-input multiple-output tensor with a maximum of four dimensions, but the batch size must always be 1. For example, if the input layer of the original deep learning network is 128-by-128-by-3, the input dimension should be either 128-by-128-by-3 or 1-by-128-by-128-by-3.

If the leading dimensions are 1 (singleton dimensions), these dimensions can often be removed without affecting compatibility. For example, if the input layer of an AI model expects an input size of 1-by-1-by-128-by-3, the input can be provided as 1-by-1-by-128-by-3 or simply 128-by-3. This is because dimensions of size 1 can be broadcast to match the expected shape.

The QNN DSP Predict block accepts either a floating-point input or a fixed-point input. The input data type must match the data type of the QNN network's input layer. Additionally, the input can be floating-point even for quantized QNN network.

Data Types: single | half | int8 | int16 | int32 | uint8 | uint16 | uint32

Output

Port_2 — Output signal after inference

n–D array

The output tensor to use for inference with the selected QNN model, represented as an n-D array, in accordance with the QNN output layer. The output data types match the data types of the QNN network's output layers.

Data Types: single | half | int8 | int16 | int32 | uint8 | uint16 | uint32

Parameters

Host(x86) QNN-model file — QNN model on host (x-86) to predict responses

<filename>.dll

Click Browse and select the QNN model, either compiled shared object (.so) or .dll, to perform inference. For details on creating an QNN model to run on device processors like DSP, refer to Qualcomm AI Engine Direct SDK documentation.

Target QNN-model file — Target QNN model on HTP (NPU) backend

<filename>.so

Click Browse and select the QNN model (compiled shared object (.so)) on the target to perform inference on the host. For details on creating an QNN model to run on device processors like DSP, refer to Qualcomm AI Engine Direct SDK documentation.

Dequantize output — Option to use output dequantization to predict response

off (default) | on

Select the checkbox to dequantize the block's output. Enabling this option results in the output data type always being single, irrespective of the deep learning neural network's output layer data type.

Programmatic Use: Add Block

To add the block programmatically, use the add_block function with one of these library block paths as the source.

"mwqnnlib/QNN DSP Predict"

Example: add_block("mwqnnlib/QNN DSP Predict","myModel/myBlock",MakeNameUnique="on")

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.

Version History

Introduced in R2026a