Optimized design of hardware accelerator based on machine learning

Abstract

Hardware accelerators are playing an increasingly important role in the field of computer science and engineering, especially in accelerating deep learning and large-scale data processing. However, the traditional hardware accelerator design often relies on manual adjustment and experience accumulation, which is inefficient. Therefore, the use of machine learning technology to optimize hardware accelerator design has become a new trend. In this paper, a hardware accelerator optimization design method based on machine learning is proposed, including hardware design, algorithm optimization, system-level optimization, and deep learning model compression. Through the research and verification of the actual case, this research proves the effectiveness and feasibility of the hardware accelerator optimization design method based on machine learning. Compared with the traditional design method, this method can find better design scheme in shorter time and has higher performance prediction accuracy. Therefore, this method can significantly improve the design efficiency and performance of hardware accelerators. The results of this study are of great significance for promoting the development of hardware accelerator technology. It can promote scientific research and engineering applications in related fields to make greater progress, and promote the development of artificial intelligence, big data processing and other fields.