Introduction to hardware accelerator systems for artificial intelligence and machine learning

An AI accelerator is a category of specialized hardware accelerator or automatic data processing system designed to accelerate computer science applications, particularly artificial neural networks, machine visualization and machine learning. Typical applications embrace algorithms for AI, Internet of things and different data-intensive or sensor-driven tasks. Machine learning is widely employed in several modern artificial intelligence applications. Varied hardware platforms are enforced to support such applications. Among them, graphics process unit (GPU) is the most widely used because of its quick computation speed and compatibility with varied algorithms. Field programmable gate arrays (FPGA) show higher energy potency as compared with GPU when computing machine learning algorithm at the cost of low speed. Varied application-specific integrated circuits (ASIC) design are projected to realize the most effective energy potency at the value of less reconfigurability that makes it appropriate for special varieties of machine learning algorithms like a deep convolutional neural network. In this chapter, we will try to relate artificial intelligence and machine learning concepts to accelerate hardware resources. Chapter will discuss software framework for Deep Neural Networks and will give comparison of FPGA, CPU and GPU. At the end of the chapter future directions and conclusion will be given.