Revolutionizing machine learning: A comprehensive analysis of ASIC hardware accelerators and their applications

Abstract

The growth of the web over the past few years has led to tremendous data growth, which has provided a powerful impetus for artificial intelligence and machine learning. Machine learning algorithms are widely used in various classification and prediction problems. However, with the rich data types and needs, the traditional software computing method that relies on CPU can no longer meet the application scenarios under different requirements. Machine Learning (ML) hardware accelerators, especially Application Specific Integrated Circuits (ASICs), have become trendy to meet a variety of needs. This paper reviews the research on ML ASIC in the past few years, reviews the development of ML and ASIC design, and summarizes the characteristics of their use. It is followed by examples of various scenarios for which it can be used, such as medical diagnostics and internet of thing (IOT) terminals. Finally, the author analyses the existing problems and limitations, and gives the improvement methods of hardware and algorithm to deal with the related obstacles. It is hoped that this paper can provide some help and convenience for the subsequent related research.