Neural networks have revolutionized the field of AI, empowering machines to acquire knowledge from data and accomplish tasks that were previously deemed unachievable. This survey covers various types of accelerators, including custom application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), graphics processing units (GPUs), and dedicated AI chips, and compares their performance, power efficiency, and scalability. The survey also discusses the design tradeoffs involved in building neural network accelerators, such as memory hierarchy, dataflow architecture, and precision. It provides insights into the latest trends and advancements in hardware accelerators for neural networks. It helps researchers, engineers, and practitioners in the field choose the right hardware platform for their specific needs and optimize the performance and energy efficiency of their neural network models. Moreover, this survey can also inspire new research directions and advanceme...

Artificial intelligence (AI) hardware accelerator is an emerging research for several applications and domains. The hardware accelerator's direction is to provide high computational speed with retaining low-cost and high learning performance. The main challenge is to design complex machine learning models on hardware with high performance. This article presents a thorough investigation into machine learning accelerators and associated challenges. It describes a hardware implementation of different structures such as convolutional neural network (CNN), recurrent neural network (RNN), and artificial neural network (ANN). The challenges such as speed, area, resource consumption, and throughput are discussed. It also presents a comparison between the existing hardware design. Last, the article describes the evaluation parameters for a machine learning accelerator in terms of learning and testing performance and hardware design.