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Sökning: id:"swepub:oai:DiVA.org:kth-307036" > A Lego-Based Neural...

A Lego-Based Neural Network Design Methodology With Flexible NoC

Chen, Kun-Chih (författare)
Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung 804, Taiwan.
Tsai, Cheng-Kang (författare)
Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung 804, Taiwan.
Liao, Yi-Sheng (författare)
Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung 804, Taiwan.
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Xu, Han-Bo (författare)
KTH,Elektronik och inbyggda system
Ebrahimi, Masoumeh (författare)
KTH,Elektronik och inbyggda system
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Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung 804, Taiwan Elektronik och inbyggda system (creator_code:org_t)
Institute of Electrical and Electronics Engineers (IEEE), 2021
2021
Engelska.
Ingår i: IEEE Journal on Emerging and Selected Topics in Circuits and Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 2156-3357 .- 2156-3365. ; 11:4, s. 711-724
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • Deep Neural Networks (DNNs) have shown superiority in solving the problems of classification and recognition in recent years. However, DNN hardware implementation is challenging due to the high computational complexity and diverse dataflow in different DNN models. 'lb mitigate this design challenge, a large body of research has focused on accelerating specific DNN models or layers and proposed dedicated designs. However, dedicated designs for specific DNN models or layers limit the design flexibility. In this work, we take advantage of the similarity among different DNN models and propose a novel Lego-based Deep Neural Network on a Chip (DNNoC) design methodology. We work on common neural computing units (e.g., multiply-accumulation and pooling) and create some neuron computing units called NeuLego processing elements (NeuLego(PE)(s)). These NeuLego(PE)(s) are then interconnected using a flexible Network-on-Chip (NoC), allowing to construct different DNN models. To support large-scale DNN models, we enhance the reusability of each NeuLego(PE) by proposing a Lego placement method. The proposed design methodology allows leveraging different DNN model implementations, helping to reduce implementation cost and time-to-market. Compared with the conventional approaches, the proposed approach can improve the average throughput by 2,802% for given DNN models. Besides, the corresponding hardware is implemented to validate the proposed design methodology, showing on average 12,523% hardware efficiency improvement by considering the throughput and area overhead simultaneously.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Datorteknik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Engineering (hsv//eng)

Nyckelord

Network on chip (NoC)
deep neural network (DNN)
accelerator

Publikations- och innehållstyp

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