Guest Editors' Introduction: High-Performance Reconfigurable Computing

Buell, Duncan A.; El‐Ghazawi, Tarek; Gaj, Kris; Kindratenko, Volodymyr

doi:10.1109/mc.2007.91

Cited by 52 publications

(24 citation statements)

References 2 publications

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“…Considering the magnetic vector potential generated by a conducting segment, the mutual inductivity can be computed using the following equation: The vectors in equation (6) are:…”

Section: Inductivity Computationmentioning

confidence: 99%

“…A logarithm, some division, addition and multiplication operations can also be observed in equation (6). Equation (1) is mainly an accumulation of mutual inductivities.…”

Section: Inductivity Computationmentioning

confidence: 99%

“…The points (x 0 , y 0 , z 0 ) and (x 1 , y 1 , z 1 ) correspond to the extremities of the first segment, while (x 2 , y 2 , z 2 ) and (x 3 , y 3 , z 3 ) correspond to the extremities of the second one. In the formulas above there is a variable t, which is a factor occurring in the integral from Equation (6). Therefore several values need to be considered for t and then the integral needs to be computed using the trapezoidal method for values in the interval (0, 1) = [0.1, 0.2, 0.3 … 0.9].…”

Section: Software Implementationmentioning

confidence: 99%

“…Logic blocks and interconnects can be programmed by the customer/designer, after the FPGA is manufactured, to implement any logical function. Their advantages include a shorter time to market, ability to reprogram in the field to fix bugs, and lower non-recurring engineering costs [6].…”

Section: Field-programmable Gate Arraysmentioning

confidence: 99%

See 3 more Smart Citations

Fpga-Based Computation of the Inductance of Coils Used for the Magnetic Stimulation of the Nervous System

Trestian

Cret

et al. 2008

Proceedings of the First International Conference on Biomedical Electronics and Devices

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In the last years the interest for magnetic stimulation of the human nervous tissue has increased considerably, because this technique has proved its utility and applicability both as a diagnostic and as a treatment instrument. Research in this domain is aimed at removing some of the disadvantages of the technique: the lack of focalization of the stimulated region and the reduced efficiency of the energetic transfer from the stimulating coil to the tissue. Better stimulation coils can solve these problems. Designing coils is so far a trial-and error process, relying on very compute-intensive simulations. In software, such a simulation has a very high running time (several hours for complicated geometries of the coils). This paper proposes and demonstrates an FPGAbased hardware implementation of this simulation which reduces the computation time by 4 orders of magnitude. Thanks to this powerful tool, some significant improvements in the design of the coils have already been obtained.

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“…Considering the magnetic vector potential generated by a conducting segment, the mutual inductivity can be computed using the following equation: The vectors in equation (6) are:…”

Section: Inductivity Computationmentioning

confidence: 99%

“…A logarithm, some division, addition and multiplication operations can also be observed in equation (6). Equation (1) is mainly an accumulation of mutual inductivities.…”

Section: Inductivity Computationmentioning

confidence: 99%

Section: Software Implementationmentioning

confidence: 99%

Section: Field-programmable Gate Arraysmentioning

confidence: 99%

See 2 more Smart Citations

Fpga-Based Computation of the Inductance of Coils Used for the Magnetic Stimulation of the Nervous System

Trestian

Cret

et al. 2008

Proceedings of the First International Conference on Biomedical Electronics and Devices

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“…Reconfigurable computing provides a high performance computing paradigm [3,4,9]. In the current development, a general reconfigurable computer comprises one or several traditional microprocessors, and reconfigurable hardware devices.…”

Section: Introductionmentioning

confidence: 99%

Function-Level Multitasking Interface Design in an Embedded Operating System with Reconfigurable Hardware

Huang

Wang

Chu

et al.

Embedded and Ubiquitous Computing

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Abstract. Reconfigurable architecture provides a high performance computing paradigm. We can implement the compute-intensive functions into reconfigurable devices to optimize the application performance. In current reconfigurable hardware designs, the function-level reconfigurable hardware has high reusability and low maintenance cost. However, the sharing mechanism and the function invocation interface are still unknown. In this paper, we propose a function-level multitasking interface design to support reconfigurable component sharing in a multitasking embedded operating system. The reconfigurable hardware functions are managed and scheduled by the operating system. Applications can use any needed hardware function via invocation APIs. To study the performance impacts, we implemented a prototype on Altera SOPC development board. We modified μC/OS-II RTOS and evaluated the prototype with prime number search programs and loop programs. The experimental results show the management overhead is acceptable.

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