Phak Len Al Eh Kan
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Phak Len Al Eh Kan
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Phak, Len Al Eh Kan
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Ehkan, Phaklen
Kan, P. L.Eh
Ehkan, Phak Len
Ehkan, P. L.
Eh Kan, P.
Eh Kan, P.
Al Eh Kan, Phak Len
Eh Kan, Phaklen
Kan, P. Eh
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Scopus Author ID
37005452000

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  • Publication
    Deep Learning with FPGA: Age and Gender Recognition for Smart Advertisement Board
    ( 2023-10-06)
    Yeoh W.S.
    ;
    Fazrul Faiz Zakaria
    ;
    Mustapa M.
    ;
    Mohd Nazri Mohd Warip
    ;
    Phak Len Al Eh Kan
    ;
    Mozi A.M.
    Age and gender recognition are helpful in various applications, especially in the field of advertising. To replace the traditional advertising method that can only display the same contents to all audiences, a smart advertisement board capable of detecting age and gender of audiences to display relevant contents is required to increase the effectiveness of advertising. This paper will use two image datasets to train and test the Convolutional Neural Network (CNN) based architecture models for age and gender recognition using deep learning. The dataset that produced the best performing model will be implemented on three different devices to observe the performance of the models on each device. A gender recognition model with accuracy of 91.53% and age recognition model with accuracy of 59.62% is produced. The results have also shown the use of Field Programmable Gated Array (FPGA) has greatly boosted the performance of the models in terms of throughput and latency.
      1  24
  • Publication
    Breast cancer classification using deep learning and FPGA inferencing
    ( 2023-02-21)
    Wong E.H.
    ;
    Fazrul Faiz Zakaria
    ;
    Mustapa M.
    ;
    Mohd Nazri Mohd Warip
    ;
    Phak Len Al Eh Kan
    Implementing deep learning technology with FPGA as an accelerator has become a popular application due to its efficiency and performance. However, given the tremendous data generated on medical diagnosis, normal inference speed is not sufficient. Hence, the FPGA technology is implemented for fast inference. In this context, the FPGA accelerates the deep learning inference process for fast breast cancer classification with minimal latency on real-time deployment. This paper summarizes the findings of model deployment across various computing devices in deep learning technology with FPGA. The study includes model performance evaluation, throughput, and latency comparison with different batch sizes to the extent of expected delay for real-world deployment. The result concludes that FPGA is the most suitable to act as a deep learning inference accelerator with a high throughput-to-latency ratio and fast parallel inference.
      1  35