Deep neural networks accelerators with focus on tensor processors

Abstract

The massive amount of data and the problem of processing them is one of the main challenges of the digital age, and the development of artificial intelligence and machine learning can be useful in solving this problem. Using deep neural networks to improve the efficiency of these two areas is a good solution. So far, several architectures have been introduced for data processing with the benefit of deep neural networks, whose accuracy, efficiency, and computing power are different from each other. This article tries to review these architectures, their features, and their functions in a systematic way. According to the current research style, 24 articles (conference and research articles related to this topic) have been evaluated in the period of 2014–2022. In fact, the significant aspects of the selected articles are compared and at the end, the upcoming challenges and topics for future research are presented. The results show that the main parameters for proposing a new tensor processor include increasing speed and accuracy and reducing data processing time, reducing on-chip storage space, reducing DRAM access, reducing energy consumption, and achieving high efficiency.