Hardware-Accelerated Computer Vision Library

Optimized vision algorithms for embedded systems and edge devices

Project Overview:

This project addresses the challenge of deploying computer vision applications on resource-constrained embedded systems and edge devices. By developing hardware-optimized implementations of common vision algorithms and providing a consistent API across different platforms, the library enables efficient vision processing on a wide range of devices.

Key Features:

  • Optimized implementations of fundamental vision operations (filtering, feature extraction, optical flow, etc.)
  • Neural network acceleration for deep learning-based vision tasks
  • Hardware-specific optimizations for various platforms (NVIDIA Jetson, Arm processors, DSPs)
  • Automatic selection of optimal implementation based on available hardware
  • Common API across all supported platforms for code portability
  • Comprehensive performance profiling and power monitoring tools
  • Support for both C++ and Python with minimal dependencies

Technical Implementation:

The library is structured as a layered architecture:

  1. Core API Layer: Provides a consistent interface for all vision operations, abstracting hardware details from the user.

  2. Algorithm Layer: Implements various vision algorithms with optimizations for numerical stability and accuracy on embedded platforms.

  3. Acceleration Layer: Contains hardware-specific implementations leveraging various acceleration technologies:
    • CUDA for NVIDIA GPUs
    • OpenCL for cross-platform GPU acceleration
    • NEON/SVE optimizations for Arm CPUs
    • DSP offloading for supported SoCs
    • Neural accelerator integration (NVDLA, NPU, etc.)
  4. Platform Layer: Handles device detection, capability querying, and optimal implementation selection.

Performance Results:

  • 5-20x speedup compared to general-purpose CPU implementations
  • 3-8x power efficiency improvement for common vision pipelines
  • Real-time performance for many vision tasks on embedded platforms:
    • 60+ FPS for 1080p image filtering on Jetson Nano
    • 30+ FPS for feature detection and tracking on Arm Cortex-A devices
    • 15+ FPS for basic neural network inference on low-power microcontrollers

Applications:

The library has been successfully applied to various edge computing applications:

  • Smart camera systems for retail analytics
  • Autonomous robot navigation
  • Augmented reality on mobile devices
  • Industrial quality control systems
  • Smart city infrastructure
  • Low-power IoT vision sensors

The project enables developers to leverage computer vision capabilities on constrained devices without requiring expertise in hardware-specific optimization, accelerating the deployment of intelligent edge applications.