Hardware Adaptation of Convolutional Neural Networks and Applications of Image Processing Algorithms

Abstract

Against the backdrop of artificial intelligence driving the deep integration of computer vision into healthcare, transportation, industrial fields and so on, the collaborative adaptation of hardware and software for Convolutional Neural Networks, called CNN, as core technology has become crucial for overcoming application performance bottlenecks. This study systematically examines the core algorithmic mechanisms and hardware implementation techniques of CNN, analyzes directions for algorithmic innovation and multi-industry deployment practices, compares performance metrics of mainstream hardware, dissects bottlenecks such as insufficient edge computing power and inadequate hardware-software adaptation, and forecasts future development trends. Research reveals that hardware evolves through a progression of “generalpurpose computing→specialized acceleration→adaptive reconfiguration.” By 2024, ASIC has captured a 42% market share, becoming the mainstream solution for data centers, with domestic chip performance approaching international standards. Algorithms are advancing toward lightweight and multimodal capabilities. China has achieved significant results in customized solutions for scenarios such as medical imaging and industrial quality inspection, while also defining tailored hardware-software adaptation strategies for different application contexts. This research provides a theoretical foundation for overcoming CNN application bottlenecks, aiding in unlocking the value of the computer vision industry and enhancing the efficiency of technology implementation in related fields.