Conference Paper
Implementation of threedimensional FPGAbased FDTD solvers: an architectural overview
EM Photonics, Inc.
DOI: 10.1109/FPGA.2003.1227265 In proceeding of: FieldProgrammable Custom Computing Machines, 2003. FCCM 2003. 11th Annual IEEE Symposium on Source: DBLP

Conference Paper: FPGABased Acceleration of the 3D FiniteDifference TimeDomain Method.
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ABSTRACT: In order to take advantage of the significant benefits afforded by computational electromagnetic techniques, such as the finitedifference timedomain (FDTD) method, solvers capable of analyzing realistic problems in a reasonable time frame are required. Although softwarebased solvers are frequently used, they are often too slow to be of practical use. To speed up computations, hardwarebased implementations of the FDTD method have been recently proposed. In this paper, we present our most recent progress in the area of FPGAbased 3D FDTD accelerators. Three aspects of the design are discussed, including the hostPC interface, memory hierarchy, and computational datapath. Implementation and benchmarking results are also presented, demonstrating that this accelerator is capable of at least threefold speedups over thirtynode PC clusters.12th IEEE Symposium on FieldProgrammable Custom Computing Machines (FCCM 2004), 2023 April 2004, Napa, CA, Proceedings; 01/2004 
Conference Paper: Hardware acceleration of the 3D finitedifference timedomain method
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ABSTRACT: Although the importance of fast, accurate computational electromagnetic (CEM) solvers is readily apparent, how to construct them is not. By nature, CEM algorithms are both computationally and memory intensive. Furthermore, the serial nature of most softwarebased implementations does not take advantage of the inherent parallelism found in many CEM algorithms. In an attempt to exploit parallelism, supercomputers and computer clusters are employed. However, these solutions can be prohibitively expensive and frequently impractical. Thus, a CEM accelerator or CEM coprocessor would provide the community with muchneeded processing power. This would enable iterative designs and designs that would otherwise be impractical to analyze. To this end, we are developing a full3D, hardwarebased accelerator for the finitedifference timedomain (FDTD) method (K.S. Yee, IEEE Trans. Antennas and Propag., vol. 14, pp. 302307, 1966). This accelerator provides speedups of up to three orders of magnitude over singlePC solutions and will surpass the throughputs of the PC clusters. In this paper, we briefly summarize previous work in this area, where it has fallen short, and how our work fills the void. We then describe the current status of this project, summarizing our achievements to date and the work that remains. We conclude with the projected results of our accelerator.Antennas and Propagation Society International Symposium, 2004. IEEE; 07/2004  [Show abstract] [Hide abstract]
ABSTRACT: Modeling, simulation and optimization using com puting tools are the core approach nowadays in science com plementary to experiment and theory. Computational Fluid Dynamics (CFD) has evolved many years ago to simulate fluid physics by solving NavierStokes equations, or its simple variants, Euler equations. However, most problems spend many hours to get solutions even with expensive supercomputers or clusters. The long computation time required for fluid dynamics simulations has lead the industry to look for some alternatives. Field Programmable Gate Arrays (FPGAs) are becoming more and more attractive for high precision scientific computations. FPGA holds the potential to alleviate this situations. It is possible for an FPGA to configure hundreds of multipliers working concurrently. In this paper, the authors explain the design on implementing the onedimensional Euler equations in hardware. Two designs with single and double floatingpoint arithmetic are developed in an FPGA. Synthesis results show that a single floatingpoint arithmetic design is consumed less area and memory usage, also operating at higher frequency. However, doubleprecision design is crucial for give a better accuracy of the result. I. INTRODUCTION01/2011;
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