D. Giannacopoulos

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Custom hardware acceleration of electromagnetics computation leverages favorable industry trends, which indicate reconfigurable hardware devices such as field-programmable gate arrays (FPGAs) may soon outperform general-purpose CPUs. We present a new striping method for efficient sparse matrix-vector multiplication implemented in a deeply pipelined FPGA(More)
Teaching undergraduate engineering electromagnetics (EM) requires extensive presentation of the basic theoretical and fundamental physical concepts that underlie most electrical engineering principles. We propose teaching fundamental EM topics through a framework that could help overcome the mentioned obstacles and challenges encountered in instruction of(More)
We develop a simulation-based approach for the computational analysis and design of dynamic load balancing algorithms in parallel three-dimensional unstructured mesh refinement with tetrahedra. A Petri Nets model is implemented based on a random polling algorithm and the target multiprocessor architecture, which simulates the behavior of the parallel mesh(More)
Communication strategies in parallel finite element methods can greatly affect system performance. The communication cost for a proposed parallel 3-D mesh refinement method with tetrahedra is analyzed. A Petri Nets-based model is developed for a target mesh refinement algorithm and parallel computing system architecture, which simulates the inter-processor(More)
Multicore systems are rapidly becoming a dominant industry trend for accelerating electromagnetics computations, driving researchers to address parallel programming paradigms early in application development. We present a new sparse representation and a two level partitioning scheme for efficient sparse matrix-vector multiplication on multicore systems, and(More)
An electromagnetic system can be described in a variety of ways. Coarse models provide fast evaluations but lack the required accuracy in the final stages of design. Fine models are highly accurate, but prohibitively expensive. Finding a compromise between these extremes may assist in overcoming bottlenecks in design automation and optimization. One(More)
In this work, a trial introduction of student projects as a mandatory part of the fundamental EM course. In particular, in order to comply with the curriculum at McGill University, the paper is limited to topics on electrostatics, magnetostatics and the slowly time-varying fields. Nonetheless, even these limitations left sufficient room to find applications(More)
Developing models from computational data is a major focus in electromagnetic design. This paper introduces ways of creating customized neural models based on a fuzzy clustering of responses. Fuzzy-clustered neural network (FCNN) models are explored, leading to increases in accuracy. The information contained within FCNN models can also be applied to space(More)
Custom hardware acceleration of electromagnetics computations leverages favorable industry trends, which indicate reconfigurable hardware devices such as field programmable gate arrays (FPGAs) may soon outperform general purpose CPUs. We present a new striping method for efficient sparse matrix-vector multiplication implemented in a deeply pipelined FPGA(More)
The potential benefits of employing optimal discretization-based refinement criteria to achieve load balancing in parallel adaptive finite-element electromagnetic analysis are considered. Specifically, the ability of this class of adaption refinement criteria to resolve an effective domain decomposition based on initial discretizations with only relatively(More)