Abstract-In this paper, we present tensor-based linear and nonlinear models for hyperspectral data classification and analysis. By exploiting principles of tensor algebra, we introduce new classification architectures, the weight parameters of which satisfy the rank-1 canonical decomposition property. Then, we propose learning algorithms to train both linear and non-linear classifiers. The advantages of the proposed classification approach are that i) it significantly reduces the number of weight parameters required to train the model (and thus the respective number of training samples), ii) it provides a physical interpretation of model coefficients on the classification output and iii) it retains the spatial and spectral coherency of the input samples. The linear tensor-based model exploits principles of logistic regression assuming the rank-1 canonical decomposition property among its weights. For the non-linear classifier, we propose a modification of a feedforward neural network (FNN), called rank-1 FNN, since its weights satisfy again the rank-1 canonical decomposition property. An appropriate learning algorithm is also proposed to train the network. Experimental results and comparisons with state of the art classification methods, either linear (e.g., Linear SVM) or non-linear (e.g., deep learning) indicates the outperformance of the proposed scheme, especially in cases where a small number of training samples is available.

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In this paper we propose a tensor-based nonlinear model for high-order data classification. The advantages of the proposed scheme are that (i) it significantly reduces the number of weight parameters, and hence of required training samples, and (ii) it retains the spatial structure of the input samples. The proposed model, called Rank-1 FNN, is based on a modification of a feedforward neural network (FNN), such that its weights satisfy the rank-1 canonical decomposition. We also introduce a new learning algorithm to train the model, and we evaluate the Rank-1 FNN on third-order hyperspectral data. Experimental results and comparisons indicate that the proposed model outperforms state of the art classification methods, including deep learning based ones, especially in cases with small numbers of available training samples.

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Abstract-To handle the stringent performance requirements of future exascale-class applications, High Performance Computing (HPC) systems need ultra-efficient heterogeneous compute nodes. To reduce power and increase performance, such compute nodes will require hardware accelerators with a high degree of specialization. Ideally, dynamic reconfiguration will be an intrinsic feature, so that specific HPC application features can be optimally accelerated, even if they regularly change over time.In the EXTRA project, we create a new and flexible exploration platform for developing reconfigurable architectures, design tools and HPC applications with run-time reconfiguration built-in as a core fundamental feature instead of an add-on. EXTRA covers the entire stack from architecture up to the application, focusing on the fundamental building blocks for run-time reconfigurable exascale HPC systems: new chip architectures with very low reconfiguration overhead, new tools that truly take reconfiguration as a central design concept, and applications that are tuned to maximally benefit from the proposed run-time reconfiguration techniques. Ultimately, this open platform will improve Europe's competitive advantage and leadership in the field.

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