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The following topics are dealt with: high-performance computing; reconfigurable computing; parallel architecture; radio astronomy signal processing; adaptive filtering; and field programmable gate array.
Reconfigurable Computing has been making inroads in the front-end digital signal processing systems deployed at radio telescopes around the world. The National Radio Astronomy Observatory (NRAO) at Green Bank has developed a signal processing system expressly for pulsar search and timing observations. These observations are among the most demanding experiments in terms of real-time computational and...
Recent work in the FPGA acceleration of molecular dynamics simulation has shown that including on-the-fly neighbor list calculation (particle filtering) in the device has the potential for an 80× per core speed-up over the CPU-based reference code and so to make the approach competitive with other computing technologies. In this paper we report on progress and challenges in advancing this work towards...
As researchers push for Exascale computing, one of the emerging challenges is system resilience. Unlike fault-tolerance which corrects errors, recent reports suggest that resilient systems will need to continue to make progress on an application despite faults. A first step in developing a resilient system is to have robust, scalable system monitoring. The work described here presents a novel, minimally-invasive...
Field-programmable gate arrays (FPGAs) can provide an efficient programmable resource for implementing hardware-based spiking neural networks (SNN). In this paper we present a hardware-software design that makes it possible to simulate large-scale (2 million neurons) biologically plausible SNNs on an FPGA-based system. We have chosen three SNN models from the various models available in the literature,...
The computing market constantly experiences the introduction of new devices, architectures, and enhancements to existing ones. Due to the number and diversity of processor and accelerator devices available, it is important to be able to objectively compare them based upon their capabilities regarding computation, I/O, power, and memory interfacing. This paper presents an extension to our existing...
Information-theoretic cost functions such as minimization of the error entropy (MEE) can extract more structure from the error signal, yielding better results in many realistic problems. However, adaptive filters (AFs) using MEE methods are more computationally intensive when compared to conventional, mean-squared error (MSE) methods employed in the well-known, least mean squares (LMS) algorithm....
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