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Author: Muñío Gracia, Alberto
Year: Since 2002
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On the Balanced Allocation of Convolutional Neural Network Models on FPGAs
A. Muñío-Gracia, J. Fernández-Berni, R. Carmona-Galán and A. Rodríguez-Vázquez
Conference - Workshop on the Architecture of Smart Cameras WASC 2019
[abstract]
Deep Learning (DL) algorithms have demonstrated their competence in accurately extracting information from data, especially in the field of computer vision. DL has emerged as an end-to-end approach based on learned multi-level scene representations. A number of open-source frameworks have been created to describe convolutional neural network (CNN) models -a class of the deep neural networks (DNNs) that support DL. Their computational complexity prompts for hardware acceleration. The challenge in the design of hardware accelerators for CNNs is providing a sustained throughput with low power consumption. In order to test our architectural proposals, we will be employing FPGAs. They are reconfigurable, efficient, and have adjustable precision. FPGAs permit architectural exploration with shorter development time and lower cost than ASICs. This work introduces an scalable, frameworkagnostic, architecture whose behavior self-adapts to the selected CNN configuration. A design space analysis is performed for some state-of-the-art CNNs, namely VGG-16, Tiny DarkNet, and SqueezeNet. The objective is a balanced allocation of resources. For this, tiling parameterization will be optimized attending to decisive performance criteria such as the number of memory accesses, data movement policy and throughput.

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