1 Ocak 2021 Konferans bildirisi Açık Erişim

Nojehdeh, Mohammadreza Esmali; Parvin, Sajjad; Altun, Mustafa

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        "affiliation": "Istanbul Tech Univ, Dept Elect & Commun Engn, TR-34469 Istanbul, Turkey", 
        "name": "Nojehdeh, Mohammadreza Esmali"
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        "affiliation": "Istanbul Tech Univ, Dept Elect & Commun Engn, TR-34469 Istanbul, Turkey", 
        "name": "Parvin, Sajjad"
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        "affiliation": "Istanbul Tech Univ, Dept Elect & Commun Engn, TR-34469 Istanbul, Turkey", 
        "name": "Altun, Mustafa"
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    "description": "In this paper, we propose an efficient method to realize a convolution layer of the convolution neural networks (CNNs). Inspired by the hilly-connected neural network architecture, we introduce an efficient computation approach to implement convolution operations. Also, to reduce hardware complexity, we implement convolutional layers under the time-multiplexed architecture where computing resources are re-used in the multiply-accumulate (MAC) blocks. A comprehensive evaluation of convolution layers shows using our proposed method when compared to the conventional MAC-based method results up to 97% and 50% reduction in dissipated power and computation time, respectively.", 
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      "title": "2021 IEEE COMPUTER SOCIETY ANNUAL SYMPOSIUM ON VLSI (ISVLSI 2021)"
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    "title": "Efficient Hardware Implementation of Convolution Layers Using Multiply-Accumulate Blocks"
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