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An FPGA-Based Energy-Efficient Reconfigurable Convolutional Neural Network Accelerator for Object Recognition Applications
Li, Jixuan1; Un, Ka Fai1; Yu, Wei Han1; Mak, Pui In1; Martins, Rui P.2,3
2021-09
Source PublicationIEEE Transactions on Circuits and Systems II: Express Briefs
ISSN1549-7747
Volume68Issue:9Pages:3143-3147
Abstract

The computational efficiency is the prime concern of a computation-intensive deep convolutional neural network (CNN). In this Brief, we report an FPGA-based computation-efficient reconfigurable CNN accelerator. It innovates in the utilization of a kernel partition technique to substantially reduce the repeated access to the input feature maps and the kernels. As a result, it balances the ability for parallel computing while consuming less system power. Experimental results prove that the proposed CNN accelerator achieves a peak throughput of 220.0 GOP/s with an energy efficiency of 22.9 GOPs/W at 151.4 frames/s for the AlexNet. It is also reconfigurable to process VGG-16 befitting complex object recognition.

KeywordComputation Efficiency Convolutional Neural Network (Cnn) Fpga Object Recognition Reconfigurability
DOI10.1109/TCSII.2021.3095283
URLView the original
Indexed BySCIE ; CPCI-S
Language英語English
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000692209000026
Scopus ID2-s2.0-85114611737
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Citation statistics
Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionINSTITUTE OF MICROELECTRONICS
Faculty of Science and Technology
DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING
Corresponding AuthorUn, Ka Fai
Affiliation1.State- Key Laboratory of Analog and Mixed-Signal VLSI/IME, DECE, Faculty of Science and Technology, University of Macau, Macao
2.State-Key Laboratory of Analog and Mixed- Signal VLSI/IME, DECE, Faculty of Science and Technology, University of Macau, Macao
3.Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
First Author AffilicationFaculty of Science and Technology
Corresponding Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Li, Jixuan,Un, Ka Fai,Yu, Wei Han,et al. An FPGA-Based Energy-Efficient Reconfigurable Convolutional Neural Network Accelerator for Object Recognition Applications[J]. IEEE Transactions on Circuits and Systems II: Express Briefs,2021,68(9):3143-3147.
APA Li, Jixuan,Un, Ka Fai,Yu, Wei Han,Mak, Pui In,&Martins, Rui P..(2021).An FPGA-Based Energy-Efficient Reconfigurable Convolutional Neural Network Accelerator for Object Recognition Applications.IEEE Transactions on Circuits and Systems II: Express Briefs,68(9),3143-3147.
MLA Li, Jixuan,et al."An FPGA-Based Energy-Efficient Reconfigurable Convolutional Neural Network Accelerator for Object Recognition Applications".IEEE Transactions on Circuits and Systems II: Express Briefs 68.9(2021):3143-3147.
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