Computer architecture

Mathematical analysis

Network topology

Data science

Von Neumann architecture

Artificial intelligence

Scalability

Deep learning

Physics

Personalization

Mathematics

Domain (mathematical analysis)

Optics

Big data

Operating system

Computer science

Cloud computing

Dataflow

Distributed computing

World Wide Web

Focus (optics)

Artificial neural network

Engineering

Recently, due to the availability of big data and the rapid growth of computing power, artificial intelligence (AI) has regained tremendous attention and investment. Machine learning (ML) approaches have been successfully applied to solve many problems in academia and in industry. Although the explosion of big data applications is driving the development of ML, it also imposes severe challenges of data processing speed and scalability on conventional computer systems. Computing platforms that are dedicatedly designed for AI applications have been considered, ranging from a complement to von Neumann platforms to a "must-have" and stand-alone technical solution. These platforms, which belong to a larger category named "domain-specific computing," focus on specific customization for AI. In this article, we focus on summarizing the recent advances in accelerator designs for deep neural networks (DNNs)—that is, DNN accelerators. We discuss various architectures that support DNN executions in terms of computing units, dataflow optimization, targeted network topologies, architectures on emerging technologies, and accelerators for emerging applications. We also provide our visions on the future trend of AI chip designs.

A Survey of Accelerator Architectures for Deep Neural Networks