A Survey of Accelerator Architectures for Deep Neural Networks
By Yiran Chen, Yuan Xie, Linghao Song, Fan Chen, Tianqi Tang. 2020
348 citations recorded by the source corpus.
Abstract 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.
Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/501e0b0bc563870a034b6761fefb80b3ecd13064
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In Computer Vision and Pattern Recognition
In Electrical and Electronic Engineering
Citations in the corpus
- MISIM: An End-to-End Neural Code Similarity System (2020, background): “losion of heterogeneous software and hardware will soon become an untenable problem that software developers will not be able to navigate (Ahmad et al., 2019; Batra et al., 2018; Bogdan et al., 2019; Chen et al., 2020; Deng et al., 2020; Hannigan et al., 2019) . Yet, as others have noted before us, ”
- The physics of optical computing (2023, background): “A complementary trend in the electronics community (both in CMOS and beyond-CMOS technologies), which has provided further support for the development of optical computers for neural networks, has been the development of special-purpose electronic chips for neural-network processing [37].”