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Convolutional neural network acceleration with hardware/software co-design

Journal Article


Abstract


  • Convolutional Neural Networks (CNNs) have a broad range of applications, such as image processing and natural language processing. Inspired by the mammalian visual cortex, CNNs have been shown to achieve impressive results on a number of computer vision challenges, but often with large amounts of processing power and no timing restrictions. This paper presents a design methodology for accelerating CNNs using Hardware/Software Co-design techniques, in order to balance performance and flexibility, particularly for resource-constrained systems. The methodology is applied to a gender recognition case study, using an ARM processor and FPGA fabric to create an embedded system that can process facial images in real-time.

Publication Date


  • 2018

Citation


  • A. Tzer-Yeu. Chen, M. Biglari-Abhari, K. I-Kai. Wang, A. Bouzerdoum & F. Tivive, "Convolutional neural network acceleration with hardware/software co-design," Applied Intelligence, vol. 48, (5) pp. 1288-1301, 2018.

Scopus Eid


  • 2-s2.0-85026772982

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/502

Number Of Pages


  • 13

Start Page


  • 1288

End Page


  • 1301

Volume


  • 48

Issue


  • 5

Place Of Publication


  • United States

Abstract


  • Convolutional Neural Networks (CNNs) have a broad range of applications, such as image processing and natural language processing. Inspired by the mammalian visual cortex, CNNs have been shown to achieve impressive results on a number of computer vision challenges, but often with large amounts of processing power and no timing restrictions. This paper presents a design methodology for accelerating CNNs using Hardware/Software Co-design techniques, in order to balance performance and flexibility, particularly for resource-constrained systems. The methodology is applied to a gender recognition case study, using an ARM processor and FPGA fabric to create an embedded system that can process facial images in real-time.

Publication Date


  • 2018

Citation


  • A. Tzer-Yeu. Chen, M. Biglari-Abhari, K. I-Kai. Wang, A. Bouzerdoum & F. Tivive, "Convolutional neural network acceleration with hardware/software co-design," Applied Intelligence, vol. 48, (5) pp. 1288-1301, 2018.

Scopus Eid


  • 2-s2.0-85026772982

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/502

Number Of Pages


  • 13

Start Page


  • 1288

End Page


  • 1301

Volume


  • 48

Issue


  • 5

Place Of Publication


  • United States