Monthly Archives: June 2016

Polyhedral AST Generation is More than Scanning Polyhedra

Polyhedral AST Generation is More than Scanning Polyhedra

  • Sven Verdoolaege, Tobias Grosser, and Albert Cohen. Polyhedral AST Generation is More than Scanning Polyhedra. Invited presentation at 37th annual ACM SIGPLAN conference on Programming Language Design and Implementation (PLDI), 2016.
    [BibTeX]
    @Misc{2016-06-VERDOOLAEGE,
    author = {Sven Verdoolaege and Tobias Grosser and Albert Cohen},
    title = {{Polyhedral {AST} Generation is More than Scanning Polyhedra}},
    date = {2016-06-15},
    howpublished = {Invited presentation at 37th annual ACM SIGPLAN conference on Programming Language Design and Implementation (PLDI)},
    address = {Santa Barbara, California, USA},
    date = {2016-06-13/2016-06-17},
    year = {2016}
    }

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Support Vector Motion Clustering

Support Vector Motion Clustering

  • Isah A. Lawal, Fabio Poiesi, Davide Anguita, and Andrea Cavallaro. Support Vector Motion Clustering. IEEE Transactions on Circuits and Systems for Video Technology, PP, 2016. doi:10.1109/TCSVT.2016.2580401
    [BibTeX] [Abstract]

    We present a closed-loop unsupervised clustering method for motion vectors extracted from highly dynamic video scenes. Motion vectors are assigned to non-convex homogeneous clusters characterizing direction, size and shape of regions with multiple independent activities. The proposed method is based on Support Vector Clustering (SVC). Cluster labels are propagated over time via incremental learning. The proposed method uses a kernel function that maps the input motion vectors into a high dimensional space to produce non-convex clusters. We improve the mapping effectiveness by quantifying feature similarities via a blend of position and orientation affinities. We use the Quasiconformal Kernel Transformation to boost the discrimination of outliers. The temporal propagation of the clusters? identities is achieved via incremental learning based on the concept of feature obsolescence to deal with appearing and disappearing features. Moreover, we design an on-line clustering performance prediction algorithm used as a feedback (closed-loop) that refines the cluster model at each frame in an unsupervised manner. We evaluate the proposed method on synthetic datasets and real-world crowded videos, and show that our solution outperforms state-of-the-art approaches.

    @article{2016-06-LAWAL,
    author = {Isah A. Lawal and Fabio Poiesi and Davide Anguita and Andrea Cavallaro},
    journal = {{IEEE Transactions on Circuits and Systems for Video Technology}},
    title = {{Support Vector Motion Clustering}},
    date = {2016-06-13},
    year = {2016},
    volume = {PP},
    issue = {99},
    doi = {10.1109/TCSVT.2016.2580401},
    abstract = {We present a closed-loop unsupervised clustering method for motion vectors extracted from highly dynamic video scenes. Motion vectors are assigned to non-convex homogeneous clusters characterizing direction, size and shape of regions with multiple independent activities. The proposed method is based on Support Vector Clustering (SVC). Cluster labels are propagated over time via incremental learning. The proposed method uses a kernel function that maps the input motion vectors into a high dimensional space to produce non-convex clusters. We improve the mapping effectiveness by quantifying feature similarities via a blend of position and orientation affinities. We use the Quasiconformal Kernel Transformation to boost the discrimination of outliers. The temporal propagation of the clusters? identities is achieved via incremental learning based on the concept of feature obsolescence to deal with appearing and disappearing features. Moreover, we design an on-line clustering performance prediction algorithm used as a feedback (closed-loop) that refines the cluster model at each frame in an unsupervised manner. We evaluate the proposed method on synthetic datasets and real-world crowded videos, and show that our solution outperforms state-of-the-art approaches.}
    }

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Investigation of Q-learning applied to DVFS management of a System-on-Chip

Investigation of Q-learning applied to DVFS management of a System-on-Chip

  • Anca Molnos, Suzanne Lesecq, Julien Mottin, and Diego Puschini. Investigation of Q-learning applied to DVFS management of a System-on-Chip. In Proceedings of 4th IFAC International Conference on Intelligent Control and Automation Sciences, pages 278-284, Reims, France, 2016. doi:10.1016/j.ifacol.2016.07.126
    [BibTeX] [Abstract]

    This paper presents a new Q-learning based strategy to manage Dynamic Voltage Frequency Scaling (DVFS) on a system on chip (SoC) such that the energy consumption is reduced. We address software applications with throughput constraints. The proposed Q-learning formulation has two main advantages: it has a reduced state space to limit the overhead and it embeds a new reward function tailored for throughput-constrained applications. The DVFS manager is evaluated on a test chip executing an HMAX object recognition application. We perform an experimental investigation of the main Q-learning parameters. The results suggest that the proposed method reduces the energy consumed with up to 44% at the cost of occasionally increasing the number of throughput violations, when compared to two state-of-the-art feedback controllers that address the same application domain.

    @InProceedings{2016-06-MOLNOS,
    author = {Anca Molnos and Suzanne Lesecq and Julien Mottin and Diego Puschini},
    title = {{Investigation of Q-learning applied to DVFS management of a System-on-Chip}},
    booktitle = {{Proceedings of 4th IFAC International Conference on Intelligent Control and Automation Sciences}},
    date = {2016-06-01/2016-06-03},
    year = {2016},
    address = {Reims, France},
    pages = {278-284},
    doi = {10.1016/j.ifacol.2016.07.126},
    abstract = {This paper presents a new Q-learning based strategy to manage Dynamic Voltage Frequency Scaling (DVFS) on a system on chip (SoC) such that the energy consumption is reduced. We address software applications with throughput constraints. The proposed Q-learning formulation has two main advantages: it has a reduced state space to limit the overhead and it embeds a new reward function tailored for throughput-constrained applications. The DVFS manager is evaluated on a test chip executing an HMAX object recognition application. We perform an experimental investigation of the main Q-learning parameters. The results suggest that the proposed method reduces the energy consumed with up to 44% at the cost of occasionally increasing the number of throughput violations, when compared to two state-of-the-art feedback controllers that address the same application domain.}
    }

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