Search Results - "Mkrtchan, Ilya"

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  1. 1

    Performance Analysis of Hardware Implementations of Reverse Conversion from the Residue Number System by Kuchukov, Viktor, Telpukhov, Dmitry, Babenko, Mikhail, Mkrtchan, Ilya, Stempkovsky, Alexander, Kucherov, Nikolay, Ermakova, Tatiana, Grigoryan, Marine

    Published in Applied sciences (01-12-2022)
    “…The Residue Number System (RNS) is a non-positional number system that allows parallel computations without transfers between digits. However, some operations…”
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    Journal Article
  2. 2

    Analysis of Posit and Bfloat Arithmetic of Real Numbers for Machine Learning by Romanov, Aleksandr Yu, Stempkovsky, Alexander L., Lariushkin, Ilia V., Novoselov, Georgy E., Solovyev, Roman A., Starykh, Vladimir A., Romanova, Irina I., Telpukhov, Dmitry V., Mkrtchan, Ilya A.

    Published in IEEE access (2021)
    “…Modern computational tasks are often required to not only guarantee predefined accuracy, but get the result fast. Optimizing calculations using floating point…”
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    Journal Article
  3. 3

    Real-time Object Detection with FPGA Using CenterNet by Solovyev, Roman A., Telpukhov, Dmitry V., Romanova, Irina I., Kustov, Alexander G., Mkrtchan, Ilya A.

    “…the paper proposes methodology for transferring architecture of modern neural network CenterNet to FPGA. CenterNet is a OneStage object detector that is used…”
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    Conference Proceeding
  4. 4

    Prospect of Analyzing Integrated Circuits Based on Dataset with Synthesis Results by Mkrtchan, Ilya A., Telpukhov, Dmitry V., Stempkovsky, Alexander L.

    “…When designing blocks for integrated circuits, it is crucial to understand whether the module in question meets set constraints. Area and timing are important…”
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    Conference Proceeding
  5. 5

    Hardware Implementation of Convolutional Neural Networks Based on Residue Number System by Soloviev, Roman, Telpukhov, Dmitry, Mkrtchan, Ilya, Kustov, Alexander, Stempkovskiy, Alexander

    “…The paper examines the use of residue number system (RNS) for hardware implementation of neural networks based on VLSI or FPGA. Widely known mobile neural…”
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    Conference Proceeding