Vision Algorithm Acceleration

This guide covers techniques for optimizing and accelerating computer vision algorithms on Qualcomm platforms, leveraging specialized hardware blocks and optimized libraries.

FastCV Library

Qualcomm's FastCV library provides hardware-accelerated implementations of common computer vision functions:

#include "fastcv.h"

void optimizeImageProcessing() {
    // Initialize FastCV
    fcvSetOperationMode(FASTCV_OP_PERFORMANCE);
    
    // Example: Accelerated image conversion from YUV to RGB
    fcvColorYCbCr420PlanarToRGB888u8(
        yuv_data,    // Input YUV data
        width,       // Image width
        height,      // Image height
        stride,      // YUV stride
        rgb_output,  // Output buffer
        rgb_stride   // RGB stride
    );
    
    // Example: Accelerated image resizing
    fcvScaleu8(
        input_image,      // Input image
        input_width,      // Input width
        input_height,     // Input height
        input_stride,     // Input stride
        output_image,     // Output image
        output_width,     // Output width
        output_height,    // Output height
        output_stride     // Output stride
    );
}

Key Accelerated Functions

FastCV provides optimized implementations of:

Function Category	Accelerated Operations
Image Conversion	Color space conversion, format transformations
Image Filtering	Gaussian blur, median filter, sobel operators
Feature Detection	FAST corners, Harris corners, edge detection
Matrix Operations	Multiplication, inverse, determinant
Image Arithmetic	Addition, subtraction, multiplication
Geometric Transforms	Resize, rotate, warp, perspective transform

Compute DSP Offloading

For more advanced use cases, the Hexagon DSP can be directly programmed:

#include "hexagon_types.h"
#include "hexagon_protos.h"

// Example of offloading a custom algorithm to the DSP
int dspOffloadExample() {
    // Initialize DSP communication
    hexagon_nn_config config;
    config.batches = 1;
    config.enable_training = 0;
    
    // Set up the DSP handle
    hexagon_nn_handle_t handle;
    int result = hexagon_nn_init(&handle);
    
    if (result != 0) {
        return -1;  // Failed to initialize DSP
    }
    
    // Execute custom operations on DSP
    // ...
    
    // Clean up
    hexagon_nn_teardown(handle);
    return 0;
}

Parallel Processing with HVX

Hexagon Vector eXtensions (HVX) can be leveraged for SIMD-style parallel processing:

#include <hexagon_types.h>
#include <hvx.h>

// Example of HVX vector processing
void hvxAcceleration() {
    // Data setup for HVX processing
    HVX_Vector* src = (HVX_Vector*)input_data;
    HVX_Vector* dst = (HVX_Vector*)output_data;
    
    // Enable HVX units
    HEXAGON_Enable_HVX();
    
    // Process data with HVX vector instructions
    for (int i = 0; i < vector_count; i++) {
        // Example: Vector addition
        *dst++ = Q6_V_vadd_VV(*src++, *offset);
    }
    
    // Disable HVX units to save power
    HEXAGON_Disable_HVX();
}

Using the Computer Vision SDK

Qualcomm provides additional acceleration through the Computer Vision SDK:

#include "qcv.h"

void acceleratedFeatureDetection() {
    // Initialize the SDK
    qcv_handle_t handle;
    qcv_status_t status = qcv_initialize(&handle);
    
    if (status != QCV_SUCCESS) {
        return;  // Failed to initialize
    }
    
    // Set up feature detection
    qcv_feature_detector_t detector;
    qcv_feature_detector_create(handle, &detector);
    
    // Configure detector
    qcv_feature_detector_set_type(detector, QCV_FEATURE_TYPE_ORB);
    
    // Run detection
    qcv_feature_points_t keypoints;
    qcv_feature_detector_detect(
        detector,
        input_image,
        width,
        height,
        stride,
        &keypoints
    );
    
    // Process detected features
    // ...
    
    // Clean up resources
    qcv_feature_detector_destroy(detector);
    qcv_shutdown(handle);
}

Acceleration Through NNAPI

For Android-based Qualcomm platforms, the Neural Network API provides hardware acceleration:

// Java example for NNAPI acceleration
import android.util.Size;
import androidx.camera.core.ImageAnalysis;
import androidx.camera.core.ImageProxy;
import android.media.Image;

// Image analyzer with NNAPI acceleration
class VisionAnalyzer implements ImageAnalysis.Analyzer {
    private NeuralNetworkInference mNNAPI;
    
    public VisionAnalyzer() {
        // Initialize NNAPI with accelerator preference
        mNNAPI = new NeuralNetworkInference.Builder()
            .setDevice(NeuralNetworks.PREFER_SUSTAINED_SPEED)
            .build();
            
        // Load and compile model
        mNNAPI.loadModel("model.tflite");
    }
    
    @Override
    public void analyze(ImageProxy image) {
        Image.Plane[] planes = image.getPlanes();
        
        // Process image using hardware acceleration
        ByteBuffer buffer = planes[0].getBuffer();
        Size size = new Size(image.getWidth(), image.getHeight());
        
        // Run accelerated processing
        mNNAPI.execute(buffer, size);
        
        image.close();
    }
}

Performance Tips

1. Memory Management:

   • Align buffers to cache line boundaries (typically 128 bytes)
   • Use direct, uncached memory for large transfers
   • Minimize memory copies between CPU and DSP domains

2. Algorithm Selection:

   • Choose algorithms that map well to parallel processing
   • Consider approximation algorithms when absolute precision isn't critical
   • Use fixed-point math where possible on DSP

3. Profiling and Optimization:

   • Profile with Snapdragon Profiler to identify bottlenecks
   • Batch operations to amortize overhead costs
   • Balance workloads across CPU, GPU, and DSP

Case Study: Feature Detection Optimization

Implementation	Processing Time	Power Consumption
OpenCV on CPU	25ms	100% (baseline)
FastCV on CPU	12ms	60%
FastCV with DSP	5ms	30%
Custom HVX	2ms	15%