Building Vision Processing Pipelines

This guide explains how to construct efficient vision processing pipelines on Qualcomm platforms, leveraging the heterogeneous computing architecture for optimal performance and power efficiency.

Pipeline Architecture Overview

A typical vision processing pipeline on Qualcomm platforms consists of these stages:

1. Image Acquisition: Camera input handling and frame buffering
2. Pre-processing: Resolution scaling, color conversion, and image enhancement
3. Feature Extraction: Edge detection, keypoint extraction, or neural network feature maps
4. Analysis/Inference: Running models for classification, detection, or segmentation
5. Post-processing: Filtering results and preparing for application consumption
6. Display/Output: Visualization or data transmission

Leveraging Heterogeneous Computing

Qualcomm's architecture allows distributing workloads across different computing units:

Processing Stage	Optimal Processor	Advantages
Image Acquisition	ISP	Hardware-accelerated image capture
Pre-processing	Hexagon DSP	Power-efficient vector operations
Feature Extraction	Adreno GPU	Parallel processing capabilities
Neural Network Inference	AI Engine/NPU	Specialized for neural networks
Post-processing	Kryo CPU	Flexibility for complex algorithms

Implementation Example

Here's an example showing how to implement a vision pipeline using the Qualcomm Neural Processing SDK:

#include <SNPE/SNPE.hpp>
#include <SNPE/SNPEFactory.hpp>
#include <DlSystem/DlEnums.hpp>
#include <DlSystem/String.hpp>
#include <DlSystem/TensorShape.hpp>
#include <opencv2/opencv.hpp>

int main() {
    // 1. Set up the runtime for the neural network
    std::unique_ptr<zdl::SNPE::SNPE> snpe;
    zdl::DlSystem::Runtime_t runtime = zdl::DlSystem::Runtime_t::GPU;
    
    // 2. Load the DLC (Deep Learning Container) file
    std::string dlcFilename = "model.dlc";
    zdl::DlSystem::String modelPath(dlcFilename.c_str());
    
    // 3. Create SNPE network instance
    snpe = zdl::SNPE::SNPEFactory::Instance().CreateSNPE(
        zdl::DlSystem::PlatformConfig(),
        modelPath.c_str(),
        runtime,
        zdl::DlSystem::RuntimeList(),
        "output_layer"
    );
    
    // 4. Set up camera input
    cv::VideoCapture camera(0);
    camera.set(cv::CAP_PROP_FRAME_WIDTH, 640);
    camera.set(cv::CAP_PROP_FRAME_HEIGHT, 480);
    
    // 5. Process frames in a loop
    cv::Mat frame;
    while (camera.read(frame)) {
        // 6. Pre-process the image (resize, normalize)
        cv::Mat preprocessed;
        cv::resize(frame, preprocessed, cv::Size(224, 224));
        preprocessed.convertTo(preprocessed, CV_32F, 1.0/255);
        
        // 7. Prepare input tensor
        std::unique_ptr<zdl::DlSystem::ITensor> inputTensor = 
            zdl::SNPE::SNPEFactory::getTensorFactory().createTensor(
                zdl::DlSystem::TensorShape({1, 3, 224, 224}));
        
        // 8. Copy image data to input tensor
        float* tensorData = reinterpret_cast<float*>(inputTensor->get());
        // ... (code to copy preprocessed image to tensor)
        
        // 9. Execute the neural network
        auto outputTensors = snpe->execute(inputTensor.get());
        
        // 10. Process the results
        auto outputTensor = (*outputTensors)[0];
        float* outputData = reinterpret_cast<float*>(outputTensor->get());
        
        // 11. Post-process and visualize results
        // ... (application-specific post-processing)
        
        // 12. Show results (for demonstration)
        cv::imshow("Results", frame);
        if (cv::waitKey(1) == 27) break;
    }
    
    return 0;
}

Performance Optimization Techniques

1. Memory Management

// Reuse buffers for image processing
cv::Mat processingBuffer(height, width, CV_8UC3);
cv::Mat resizedBuffer(model_height, model_width, CV_8UC3);

// Pre-allocate tensor memory
float* tensorBuffer = new float[input_size];

2. Parallel Processing

// Example: Processing multiple regions of interest in parallel
#include <thread>
#include <vector>

void processRegionOfInterest(cv::Mat& roi, int id) {
    // Process individual ROI
}

// In main processing loop:
std::vector<std::thread> threads;
for (int i = 0; i < num_rois; i++) {
    cv::Mat roi = extractROI(frame, i);
    threads.push_back(std::thread(processRegionOfInterest, roi, i));
}

for (auto& t : threads) {
    t.join();
}

3. Offloading Compute-Intensive Tasks

// Example: Offloading histogram calculation to DSP
#include "fastcv.h"

// Instead of:
// cv::calcHist(...);

// Use FastCV (optimized for Hexagon DSP):
fcvHistogram8u(...);

4. Quantization for Neural Networks

For maximum performance on Qualcomm hardware, consider using quantized models:

// During model conversion:
snpe-dlc-quantize --input_dlc model_fp32.dlc --output_dlc model_int8.dlc

Measuring Pipeline Performance

Use the Snapdragon Profiler tool to analyze:

1. Frame Processing Time: End-to-end latency for each frame
2. CPU/GPU/DSP Utilization: Distribution of workload across processing units
3. Memory Bandwidth: Data transfer efficiency between components
4. Power Consumption: Energy usage during vision tasks