Optimising Sparse Matrix Vector multiplication for large scale FEM problems on FPGA

Sparse Matrix Vector multiplication (SpMV) is an important kernel in many scientific applications. In this work we propose an architecture and an automated customisation method to detect and optimise the architecture for block diagonal sparse matrices. We evaluate the proposed approach in the context of the spectral/hp Finite Element Method, using the local matrix assembly approach. This problem leads to a large sparse system of linear equations with block diagonal matrix which is typically solved using an iterative method such as the Preconditioned Conjugate Gradient. The efficiency of the proposed architecture combined with the effectiveness of the proposed customisation method reduces BRAM resource utilisation by as much as 10 times, while achieving identical throughput with existing state of the art designs and requiring minimal development effort from the end user. In the context of the Finite Element Method, our approach enables the solution of larger problems than previously possible, enabling the applicability of FPGAs to more interesting HPC problems

Benefit salience and consumers' selective attention to product features.

Although attention is a key construct in models of marketing communication and consumer choice, its selective nature has rarely been examined in common time-pressured conditions. We focus on the role of benefit salience, that is, the readiness with which particular benefits are brought to mind by consumers in relation to a given product category. Study I demonstrated that when product feature information was presented rapidly, individuals for whom the benefit of personalised customer service had high habitual salience displayed selective attention as evidenced by elevated recall and recognition of a target feature (a bank's ''friendly employees''). Also, as expected, individual differences in habitual benefit salience affected judgements of the target product. Study 2 showed that when subjects were additionally informed about a specific product usage situation, selective attention was primarily influenced by the relevance of the target feature to benefits made salient by the usage situation; individual differences played a less important role. Discussion emphasises theoretical aspects of the findings as well as managerial implications with respect to person-situation approaches to benefit segmentation. (C) 1997 Elsevier Science B.V.attention; benefit segmentation; individual construct accessibility; usage context; involvement; segmentation; substitution; experiences; memory; recall; choice; link;

The influence of consumers' goals on selective attention to product features.

Although attention is a key construct in models of marketing communications and consumer choice, its selective nature has rarely been examined in the time-pressured conditions that consumers face everyday. We investigate how consumers' goals influence selective attention to product features under such conditions. Specifically, we focus on the role of goal salience, that is, the readiness with which particular goals (e.g., personalized customer service) are brought to mind by consumers in relation to a given product category (e.g., banks). Study1 demonstrated that when product feature information was presented rapidly, individuals for whom the goal of personalized customer service had high chronic or habitual salience displayed selective attention in terms of their elevated recall of a target feature (a bank's 'friendly employees'). Also, as expected, individual differences in chronic goal salience affected judgments of the target product. Study2 showed that when subjects were additionally informed about a specific product usage situation (e.g., being new in town or experiencing difficulty in balancing a checkbook), selective attention was no longer affected by individuals' chronic tendencies. Instead, both feature recall and judgments were influenced by the relevance of the target feature to the goals made salient by the situational context. Discussion emphasizes the theoretical and managerial implications of the findings regarding the role of goal salience in selective attention to product features.Product;

Nonlocal effective medium analysis in symmetric metal-dielectric multilayer metamaterials

The optical nonlocality in symmetric metal-dielectric multilayer metamaterials is theoretically and experimentally investigated with respect to transverse-magnetic-polarized incident light. A nonlocal effective medium theory is derived from the transfer-matrix method to determine the nonlocal effective permittivity depending on both the frequency and wave vector in a symmetric metal-dielectric multilayer stack. In contrast to the local effective medium theory, our proposed nonlocal effective medium theory can accurately predict measured incident angle-dependent reflection spectra from a fabricated multilayer stack and provide nonlocal dispersion relations. Moreover, the bulk plasmon polaritons with large wave vectors supported in the multilayer stack are also investigated with the nonlocal effective medium theory through the analysis of the dispersion relation and eigenmode.Comment: 21 pages, 7 figure

Selectivity and Metaplasticity in a Unified Calcium-Dependent Model

A unified, biophysically motivated Calcium-Dependent Learning model has been shown to account for various rate-based and spike time-dependent paradigms for inducing synaptic plasticity. Here, we investigate the properties of this model for a multi-synapse neuron that receives inputs with different spike-train statistics. In addition, we present a physiological form of metaplasticity, an activity-driven regulation mechanism, that is essential for the robustness of the model. A neuron thus implemented develops stable and selective receptive fields, given various input statistic