A Survey of Accelerator Architectures for Deep Neural Networks
(چکیده مقاله) :
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
(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
(توضیحات تکمیلی) :
(توضیحات تکمیلی) :
Description :
مقاله ISI انگلیسی اصلی
سال انتشار: 2020
فایل ISI انگلیسی اصلی ، با فرمت Pdf
تعداد صفحات فایل ISI انگلیسی اصلی: 11 صفحه
سال انتشار: 2020
فایل ISI انگلیسی اصلی ، با فرمت Pdf
تعداد صفحات فایل ISI انگلیسی اصلی: 11 صفحه
Authors / Descriptions(نویسندگان/توضیحات): سال انتشار 2020 \ مقاله ISI انگلیسی اصلی \ نویسندگان: Yiran Chen a,⇑, Yuan Xie b, Linghao Song a, Fan Chen a, Tianqi Tang
Sent date(تاریخ ارسال) :
1399/06/09 | 8/30/2020
Number of visits(تعداد بازدید):
646
Key words (کلمات کلیدی):
Deep neural network , Domain-specific architecture , Accelerator
Number of pages(تعداد صفحات) :
11
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