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Karwatowski, Michał

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nieaktywny

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informatyka techniczna i telekomunikacja
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ORCID 0000-0001-6285-136X 
ScopusID 57189294331 
PBN - Polish Scientific Bibliography 
Systemy AGH
Bibliografia: BaDAP AGH 
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  • thumbnail.journal.alt
    Item type:Article, Access status: Open Access ,
    Compressing sentiment analysis CNN models for efficient hardware processing
    (Wydawnictwa AGH, 2020) Wróbel, Krzysztof; Karwatowski, Michał; Wielgosz, Maciej; Pietroń, Marcin; Wiatr, Kazimierz
    Convolutional neural networks (CNNs) were created for image classification tasks. Shortly after their creation, they were applied to other domains, including natural language processing (NLP). Nowadays, solutions based on artificial intelligence appear on mobile devices and embedded systems, which places constraints on memory and power consumption, among others. Due to CNN memory and computing requirements, it is necessary to compress them in order to be mapped to the hardware. This paper presents the results of the compression of efficient CNNs for sentiment analysis. The main steps involve pruning and quantization. The process of mapping the compressed network to an FPGA and the results of this implementation are described. The conducted simulations showed that the 5-bit width is enough to ensure no drop in accuracy when compared to the floating-point version of the network. Additionally, the memory footprint was significantly reduced (between 85 and 93% as compared to the original model).
  • thumbnail.journal.alt
    Item type:Article, Access status: Open Access ,
    FPGA implementation of procedures for video quality assessment
    (Wydawnictwa AGH, 2018) Wielgosz, Maciej; Karwatowski, Michał; Pietroń, Marcin; Wiatr, Kazimierz
    The video resolutions used in a variety of media are constantly rising. While manufacturers struggle to perfect their screens, it is also important to ensure the high quality of the displayed image. Overall quality can be measured using a Mean Opinion Score (MOS). Video quality can be affected by miscellaneous artifacts appearing at every stage of video creation and transmission. In this paper, we present a solution to calculate four distinct video quality metrics that can be applied to a real-time video quality assessment system. Our assessment module is capable of processing 8K resolution in real time set at a level of 30 frames per second. The throughput of 2.19 GB/s surpasses the performance of pure software solutions. The module was created using a high-level language to concentrate on architectural optimization.