Convolutional neural networks, hardware accelerators, computational efficiency, energy consumption, system optimization
Abstract
With the expansion of Convolutional Neural Networks (CNNs) applications in various domains such as autonomous driving and real-time data processing, the demand for efficient computational resources has increased dramatically. Traditional computing platforms such as CPUs struggle to manage the complex and data-intensive tasks required by modern CNNs. This paper delves into the development and system optimization of dedicated hardware accelerators - GPUs, FPGAs, and ASICs to meet these demands. Through innovative architectural design and optimization techniques, these gas pedals improve computational speed and energy efficiency. Our study demonstrates that through these optimizations, the processing efficiency of hardware accelerators is significantly improved while energy consumption is effectively controlled, setting a new standard for the design of future hardware accelerators for convolutional neural networks. In addition, I discuss practical applications and future challenges, providing a comprehensive overview of current technologies and their potential development. This research highlights the critical role of advanced hardware accelerators in enabling the next generation of AI applications, ensuring technological advancement and sustainability in high-demand computing environments.
