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Author: Velasco Montero , Delia
Year: Since 2002
All publications
On-The-Fly Deployment of Deep Neural Networks on Heterogeneous Hardware in a Low-Cost Smart Camera
D. Velasco-Montero, J. Fernández-Berni, R. Carmona-Galan and A. Rodríguez-Vázquez
Conference - ACM International Conference on Distributed Smart Cameras ICDSC 2018
DOI: 10.1145/3243394.3243705    » doi
[abstract]
This demo showcases a low-cost smart camera where different hardware configurations can be selected to perform image recognition on deep neural networks. Both the hardware configuration and the network model can be changed any time on the fly. Up to 24 hardware-model combinations are possible, enabling dynamic reconfiguration according to prescribed application requirements.

Optimum Network/Framework Selection from High-Level Specifications in Embedded Deep Learning Vision Applications
D. Velasco-Montero, J. Fernández-Berni, R. Carmona-Galán and A. Rodríguez-Vázquez
Journal Paper - Lecture Notes in Computer Science LNCS, vol. 11182, pp 369-379, 2018
SPRINGER    DOI: 10.1007/978-3-030-01449-0_31    ISSN: 0302-9743    » doi
[abstract]
This paper benchmarks 16 combinations of popular Deep Neural Networks and Deep Learning frameworks on an embedded platform. A Figure of Merit based on high-level specifications is introduced. By sweeping the relative weight of accuracy, throughput and power consumption on global performance, we demonstrate that only a reduced set of the analyzed combinations must actually be considered for real deployment. We also report the optimum network/framework selection for all possible application scenarios defined in those terms, i.e. weighted balance of the aforementioned parameters. Our approach can be extended to other networks, frameworks and performance parameters, thus supporting system-level design decisions in the ever-changing ecosystem of Deep Learning technology.

Optimum Network/Framework Selection from High-Level Specifications in Embedded Deep Learning Vision Applications
D. Velasco-Montero, J. Fernández-Berni, R. Carmona-Galán and A. Rodríguez-Vázquez
Conference - Advanced Concepts for Intelligent Vision Systems ACIVS 2018
[abstract]
This paper benchmarks 16 combinations of popular Deep Neural Networks and Deep Learning frameworks on an embedded platform. A Figure of Merit based on high-level specifications is introduced. By sweeping the relative weight of accuracy, throughput and power consumption on global performance, we demonstrate that only a reduced set of the analyzed combinations must actually be considered for real deployment. We also report the optimum network/framework selection for all possible application scenarios defined in those terms, i.e. weighted balance of the aforementioned parameters. Our approach can be extended to other networks, frameworks and performance parameters, thus supporting system-level design decisions in the ever-changing ecosystem of Deep Learning technology.

Performance Analysis of Real-Time DNN Inference on Raspberry Pi
D. Velasco-Montero, J. Fernández-Berni, R. Carmona-Galán and A. Rodríguez-Vázquez
Conference - SPIE Real-Time Image and Video Processing 2018
[abstract]
Deep Neural Networks (DNNs) have emerged as the reference processing architecture for the implementation of multiple computer vision tasks. They achieve much higher accuracy than traditional algorithms based on shallow learning. However, it comes at the cost of a substantial increase of computational resources. This constitutes a challenge for embedded vision systems performing edge inference as opposed to cloud processing. In such a demanding scenario, several open-source frameworks have been developed, e.g. Ca e, OpenCV, TensorFlow, Theano, Torch or MXNet. All of these tools enable the deployment of various state-of-the-art DNN models for inference, though each one relies on particular optimization libraries and techniques resulting in di erent performance behavior. In this paper, we present a comparative study of some of these frameworks in terms of power consumption, throughput and precision for some of the most popular Convolutional Neural Networks (CNN) models. The benchmarking system is Raspberry Pi 3 Model B, a low-cost embedded platform with limited resources. We highlight the advantages and limitations associated with the practical use of the analyzed frameworks. Some guidelines are provided for suitable selection of a speci c tool according to prescribed application requirements.

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