for energy efficiency. This paper presents the DART architecture based on the functionallevel reconfiguration paradigm which allows a significant improvement in energy efficiency. DART is built around a hierarchical interconnection network allowing high flexibility while keeping the power overhead low

Reconfigurable computing is emerging as the new paradigm for satisfying the simultaneous demand for application performance and flexibility. The ability to customize the architecture to match the computation and the dataflow of the application has demonstrated significant performance benefits

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microswitch—perhaps the paradigm RF-MEMS device. Through its superior performance characteristics, the microswitch is being developed in a number of existing circuits and systems, including radio front-ends, capac-itor banks, and time-delay networks. The superior performance combined with ultra

the relationship between the notions of coordination and (dynamic) configuration and we illustrate the potential of control- or eventdriven coordination languages to be used as languages for expressing dynamically reconfigurable software architectures. We argue that control-driven coordination has similar goals

the relationship between the notions of coordination and (dynamic) configuration and we illustrate the potential of control- or event-driven coordination languages to be used as languages for expressing dynamically reconfigurable software architectures. We argue that control-driven coordination has similar goals

A digital VLSI implementation of linear template CNNs is presented. The reconfigurable architecture is organized as 12 layers of 64\Theta64 cells. The CNNs are reformulated introducing sets of generalized cloning templates to enucleate more sharply the structure of both intra- and inter

All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.

Reconfigurable computing has become an essential part of research in the domain of modern computing paradigms. Reconfigurable computing approach integrates both, the performance and flexibility gaining aspects on a single computing system. The computational performance of such kind of systems

Artificial neural network paradigms have shown the capabilities of performing input-output mapping operations even where the transformation rules are not formally defined, are partially known, or are ill-defined. For high speed processing of such information, hardware implementation sarerequired