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The usage of FPGAs for the acceleration of Convolutional Neuronal Nets (CNNs) with OpenCL. Two alternatives for implementation - Couverture souple
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Seminar paper from the year 2018 in the subject Engineering - Computer Engineering, grade: 1.0, University of Paderborn, language: English, abstract: Convolutional Neuronal Nets (CNNs) are state-of-the art Neuronal Networks, which are used in many fields like video analysis, face detection or image classification. Due to high requirements regarding computational resources and memory bandwidth, CNNs are mainly executed on special accelerator hardware which is more powerful and energy efficient than general purpose processors. This paper will give an overview of the usage of FPGAs for the acceleration of computation intensive CNNs with OpenCL, proposing two different implementation alternatives. The first approach is based on nested loops, which are inspired by the mathematical formula of multidimensional convolutions. The second strategy transforms the computational problem into a matrix multiplication problem on the fly. The approaches are followed by common optimization techniques used for FPGA designs based on high level synthesis (HLS). Afterwards, the proposed implementations are compared to a CNN implementation on an Intel Xeon CPU in order to demonstrate the advantages in terms of performance and energy efficiency.
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The usage of FPGAs for the acceleration of Convolutional Neuronal Nets (CNNs) with OpenCL. Two alternatives for implementation
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The usage of FPGAs for the acceleration of Convolutional Neuronal Nets (CNNs) with OpenCL. Two alternatives for implementation (Paperback or Softback)
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The usage of FPGAs for the acceleration of Convolutional Neuronal Nets (CNNs) with OpenCL. Two alternatives for implementation
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Grin Verlag, 2019
ISBN 10 : 3668861307
ISBN 13 : 9783668861305
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The usage of FPGAs for the acceleration of Convolutional Neuronal Nets (CNNs) with OpenCL. Two alternatives for implementation
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Seminar paper from the year 2018 in the subject Engineering - Computer Engineering, grade: 1.0, University of Paderborn, language: English, abstract: Convolutional Neuronal Nets (CNNs) are state-of-the art Neuronal Networks, which are used in many fields like video analysis, face detection or image classification. Due to high requirements regarding computational resources and memory bandwidth, CNNs are mainly executed on special accelerator hardware which is more powerful and energy efficient than general purpose processors. This paper will give an overview of the usage of FPGAs for the acceleration of computation intensive CNNs with OpenCL, proposing two different implementation alternatives.The first approach is based on nested loops, which are inspired by the mathematical formula of multidimensional convolutions. The second strategy transforms the computational problem into a matrix multiplication problem on the fly. The approaches are followed by common optimization techniques used for FPGA designs based on high level synthesis (HLS). Afterwards, the proposed implementations are compared to a CNN implementation on an Intel Xeon CPU in order to demonstrate the advantages in terms of performance and energy efficiency. 24 pp. Englisch. N° de réf. du vendeur 9783668861305
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The usage of FPGAs for the acceleration of Convolutional Neuronal Nets (CNNs) with OpenCL. Two alternatives for implementation
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The usage of FPGAs for the acceleration of Convolutional Neuronal Nets (CNNs) with OpenCL. Two alternatives for implementation
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ISBN 10 : 3668861307
ISBN 13 : 9783668861305
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Seminar paper from the year 2018 in the subject Engineering - Computer Engineering, grade: 1.0, University of Paderborn, language: English, abstract: Convolutional Neuronal Nets (CNNs) are state-of-the art Neuronal Networks, which are used in many fields like video analysis, face detection or image classification. Due to high requirements regarding computational resources and memory bandwidth, CNNs are mainly executed on special accelerator hardware which is more powerful and energy efficient than general purpose processors. This paper will give an overview of the usage of FPGAs for the acceleration of computation intensive CNNs with OpenCL, proposing two different implementation alternatives. The first approach is based on nested loops, which are inspired by the mathematical formula of multidimensional convolutions. The second strategy transforms the computational problem into a matrix multiplication problem on the fly. The approaches are followed by common optimization techniques used for FPGA designs based on high level synthesis (HLS). Afterwards, the proposed implementations are compared to a CNN implementation on an Intel Xeon CPU in order to demonstrate the advantages in terms of performance and energy efficiency.Books on Demand GmbH, Überseering 33, 22297 Hamburg 24 pp. Englisch. N° de réf. du vendeur 9783668861305
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Seminar paper from the year 2018 in the subject Engineering - Computer Engineering, grade: 1.0, University of Paderborn, language: English, abstract: Convolutional Neuronal Nets (CNNs) are state-of-the art Neuronal Networks, which are used in many fields like video analysis, face detection or image classification. Due to high requirements regarding computational resources and memory bandwidth, CNNs are mainly executed on special accelerator hardware which is more powerful and energy efficient than general purpose processors. This paper will give an overview of the usage of FPGAs for the acceleration of computation intensive CNNs with OpenCL, proposing two different implementation alternatives.The first approach is based on nested loops, which are inspired by the mathematical formula of multidimensional convolutions. The second strategy transforms the computational problem into a matrix multiplication problem on the fly. The approaches are followed by common optimization techniques used for FPGA designs based on high level synthesis (HLS). Afterwards, the proposed implementations are compared to a CNN implementation on an Intel Xeon CPU in order to demonstrate the advantages in terms of performance and energy efficiency. N° de réf. du vendeur 9783668861305
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The usage of FPGAs for the acceleration of Convolutional Neuronal Nets (CNNs) with OpenCL. Two alternatives for implementation
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Taschenbuch. Etat : Neu. The usage of FPGAs for the acceleration of Convolutional Neuronal Nets (CNNs) with OpenCL. Two alternatives for implementation | Christian Lienen | Taschenbuch | 24 S. | Englisch | 2019 | GRIN Verlag | EAN 9783668861305 | Verantwortliche Person für die EU: GRIN Publishing GmbH, Waltherstr. 23, 80337 München, info[at]grin[dot]com | Anbieter: preigu Print on Demand. N° de réf. du vendeur 115307338
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