HW/SW Co-'esign for Dates Classification on Xilinx Zynq SoC

Ahmed Chiheb Ammari, Lazhar Khriji, Medhat Awadalla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


This paper proposes HW/SW Co-design of an automatic classification system of Khalas, Khunaizi, Fardh, Qash, Naghal, and Maan dates fruit varieties in Oman. The system implements pre-processing, segmentation of the colored input images, color and shape-size features extraction followed by ANN-tansig classification. The performance of the proposed system is experimented and 97.26% highest classification accuracy are achieved. The proposed system is prototyped using a selected Zynq 7020 SoC platform featuring, on the same chip, a dual-core ARM Cortex A9 processing System (PS) interconnected with FPGA logic (PL) though high-throughput communication channels. The original classification algorithm is profiled and then a HW/SW Co-design is developed achieving 10.9 fps real time classification performance. This performance is acceptable and represents almost 14 times speedup acceleration comparatively to the original program implementation.

Original languageEnglish
Title of host publicationProceedings of the 26th Conference of Open Innovations Association FRUCT, FRUCT 2020
EditorsSergey Balandin, Ilya Paramonov, Tatiana Tyutina
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9789526924427
Publication statusPublished - Apr 2020
Event26th Conference of Open Innovations Association FRUCT, FRUCT 2020 - Yaroslavl, Russian Federation
Duration: Apr 23 2020Apr 24 2020

Publication series

NameConference of Open Innovation Association, FRUCT
ISSN (Print)2305-7254


Conference26th Conference of Open Innovations Association FRUCT, FRUCT 2020
Country/TerritoryRussian Federation

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering

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