کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
528843 869613 2016 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Rate-energy-accuracy optimization of convolutional architectures for face recognition
ترجمه فارسی عنوان
بهینه سازی دقت ـ سرعت ـ انرژی معماری کانولوشن برای تشخیص چهره
کلمات کلیدی
معماری انقباضی؛ شبکه عصبی مصنوعی (CNNs)؛ بهينه سازي؛ رمزگذاری؛ تشخیص چهره؛ آنالیز و سپس فشرده سازی (ATC)؛ یادگیری عمیق؛ شبکه های عصبی عمیق
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی


• We optimize the energy-rate-accuracy characteristics of a convolutional architecture for face recognition.
• We profile a convolutional network implementation on low-power hardware, i.e., a Raspberry Pi.
• We obtain a speedup of 17 without significantly affecting task accuracy.
• We propose a coding architecture tailored to features extracted by such model, obtaining very compact yet effective features.

Face recognition systems based on Convolutional Neural Networks (CNNs) or convolutional architectures currently represent the state of the art, achieving an accuracy comparable to that of humans. Nonetheless, there are two issues that might hinder their adoption on distributed battery-operated devices (e.g., visual sensor nodes, smartphones, and wearable devices). First, convolutional architectures are usually computationally demanding, especially when the depth of the network is increased to maximize accuracy. Second, transmitting the output features produced by a CNN might require a bitrate higher than the one needed for coding the input image. Therefore, in this paper we address the problem of optimizing the energy-rate-accuracy characteristics of a convolutional architecture for face recognition. We carefully profile a CNN implementation on a Raspberry Pi device and optimize the structure of the neural network, achieving a 17-fold speedup without significantly affecting recognition accuracy. Moreover, we propose a coding architecture custom-tailored to features extracted by such model.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Visual Communication and Image Representation - Volume 36, April 2016, Pages 142–148
نویسندگان
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