Tensor-based Nonlinear Classifier for High-Order Data Analysis

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 \textit{Rank}-1 FNN, is based on a modification of a feedforward neural network (FNN), such that its weights satisfy the {\it rank}-1 canonical decomposition. We also introduce a new learning algorithm to train the model, and we evaluate the \textit{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.Comment: To appear in IEEE ICASSP 2018. arXiv admin note: text overlap with arXiv:1709.0816

EXTRA: Towards an efficient open platform for reconfigurable High Performance Computing

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

Co-designed Innovation and System for Resilient Exascale Computing in Europe: From Applications to Silicon (EuroEXA)

EuroEXA targets to provide the template for an upcoming exascale system by co-designing and implementing a petascale-level prototype with ground-breaking characteristics. To accomplish this, the project takes a holistic approach innovating both across the technology and the application/system software pillars. EuroEXA proposes a balanced architecture for compute and data-intensive applications, that builds on top of cost-efficient, modular-integration enabled by novel inter-die links, utilises a novel processing unit and embraces FPGA acceleration for computational, networking and storage operations. EuroEXA hardware designers work together with system software experts optimising the entire stack from language runtimes to low-level kernel drivers, and application developers that bring in a rich mix of key HPC applications from across climate/weather, physical/energy and life-science/bioinformatics domains to enable efficient system co-design and maximise the impact of the project