Research on Hardware Acceleration Optimisation Strategies for Deep Learning in Computer Vision

Abstract

As deep neural network (DNN) models get larger and more complicated, the importance of hardware acceleration becomes more and more apparent. This paper discusses various hardware acceleration strategies for deep learning, especially in the area of computer vision. It explores the use of GPUs, FPGAs, and ASICs, detailing their respective strengths and weaknesses in accelerating DNNs. This paper argues that the future of DNN hardware acceleration lies in hybrid approaches that combine the advantages of different architectures. Software advances such as improved compilers and synthesis tools will also play a critical role in making these techniques more accessible. By utilizing the appropriate hardware technology for a given task and continuing to innovate in both hardware and software, computer vision will make significant advances in performance, efficiency, and scalability. This hybrid approach is key to the future of DNN hardware acceleration, offering a path to overcome the limitations of any single type of hardware.