Exploring TensorFlow Lite delegates for prototyping

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The Getting started with AI on NXP i.MX8M Plus guide is currently under active development. Some sections may be incomplete or change without notice.

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Getting started with AI on NXP i.MX8M Plus
Introduction
Overview i.MX8M Plus hardware specifications Development tools Development kit DART-MX8M-PLUS Development kit Quick getting started Yocto minimal image Flashing SD card First steps on the board
Development
Gatesgarth and Hardknott Yocto versions Downloading Yocto images for NXP based BSP Building an imx-core-image Flashing SD card Integrating multimedia software stack Installing GStreamer Installing OpenCV Integrating Artificial Intelligence software stack Installing TensorFlow Lite for NXP based images Installing R2Inference Installing GstInference Developing software for the board Building the SDK Crosscompiling apps for GStreamer, TensorFlow Lite, and OpenCV Exploring TensorFlow Lite delegates for prototyping
Neural Processing Unit
Introduction Profiling the NPU Installing gputop Setting the board on debug mode Use Case experiments: Smart Parking Introduction to the use case License Plate Detection TinyYOLO version 3 Training a model compatible with GstInference License Plate Recognition Rosetta Getting the model prototyping Bash scripts for CPU usage and time estimation Serial experiments Parallel Experiments Results
GStreamer
Exploring Rosetta element for character recognition Example pipelines Smart Parking Video inferences Image inferences Video streaming
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NNAPI Delegate

With this delegate, we are able to ship the inference to the NPU accelerator. You need to ensure your model supports 8 or 16 bits quantization, otherwise, the NNAPI will send the unsupported operation back to the CPU, executing a CPU fallback, decreasing the performance of the entire inference time execution, as we will see in further sections. Also, you had to enable the TensorFlow Lite construction with NNAPI -DTFLITE_ENABLE_NNAPI=on flag for this step.

As we saw in the Cross-compiling apps for GStreamer, TensorFlow_Lite, and OpenCV in the minimal TensorFlow Lite example, then the process of delegating is related to adding the following lines before allocating the input tensors:

// <Your includes>

// The required includes for NNAPI:
#include "tensorflow/lite/delegates/nnapi/nnapi_delegate.h"
#include "tensorflow/lite/tools/delegates/delegate_provider.h"

void inference(){
      
      // <Interpreter construction>

      // NNAPI construction:
      tflite::StatefulNnApiDelegate::Options options;
      options.allow_fp16 = true;
      options.allow_dynamic_dimensions = true;
      options.disallow_nnapi_cpu = false;
      options.accelerator_name = "vsi-npu";
      auto delegate = tflite::evaluation::CreateNNAPIDelegate(options);
      
      if (!delegate){
        std::cout << "NNAPI delegate is not well created \n" << std::endl;
        return ; 
      } else {
        // Modifying the graph to support NNAPI operations: 
        interpreter->ModifyGraphWithDelegate(std::move(delegate));
        // Allocating the input thensors:
        TFLITE_MINIMAL_CHECK2(interpreter->AllocateTensors() == kTfLiteOk);

        // <Feed your tensors>
      }
}