ABSTRACT
Machine learning is a common research hotspot in the fields of artificial intelligence and pattern recognition, and its theories and methods have been widely used to solve complex problems in engineering applications and scientific fields. This chapter investigates and combs common machine learning algorithms, and gives a typical solution for hardware customization of such algorithms based on Field-Programmable Gate Array (FPGAs). This chapter consists of three parts: the first part is an overview of machine learning, in which the algorithmic principles of different machine learning algorithms are briefly introduced and categorized in a general way; the second part introduces the design methodology of machine learning accelerators based on FPGAs, and categorizes and summarizes the representative accelerator design work for the relevant algorithms; and the third part gives a conclusive account of the hardware customization of the machine learning algorithms in question summary, and further research directions in this area are analyzed.
