Efficient processing of 3D image data is one of the biggest challenges in biological image analysis. Single plane illumination microscopy, for instance, allows acquiring massive amounts of 3D image data that needs to ...
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ISBN:
(纸本)9781479923519
Efficient processing of 3D image data is one of the biggest challenges in biological image analysis. Single plane illumination microscopy, for instance, allows acquiring massive amounts of 3D image data that needs to be processed automatically. An important preprocessing step is deconvolution which reduces the image blur introduced by the imaging system. An often used algorithm is Richardson-Lucy deconvolution which, however, is very time-consuming for huge amounts of 3D image data. We have developed an FPGA-based acceleration of Richardson-Lucy deconvolution. Compared to CPU architectures the computation time is reduced.
For maintaining high performance and minimizing power consumption, adaptive, heterogeneous many-core architectures can be adapted at runtime to changing environmental requests or conditions as well as to changes resul...
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For maintaining high performance and minimizing power consumption, adaptive, heterogeneous many-core architectures can be adapted at runtime to changing environmental requests or conditions as well as to changes resulting from the dynamics of the workload itself. However, the huge complexity of such architectures makes their optimization very challenging at runtime. This challenge is therefore addressed within this paper by an Organic Computing approach for realizing a proactive, self-optimizing system behavior within adaptive, heterogeneous systems using a light-weight Learning Classifier System and a Run Length Encoding Markov predictor. The first realizes a self-optimizing behavior, freeing the user from the burden of optimizing the system manually, and the latter captures the system behavior, permits prediction of future system states, and therefore permits exploiting regular behavior for further improving the overall system performance. Using the use case of optimizing the overall system performance, results showed that the proactive, self-optimizing system achieved a performance improvement of 11.3% in comparison to a non-optimizing system.
Self-organizing principles can address the growing complexity and the huge challenge of management and efficient utilization of adaptive many-core architectures. Fundamental for realizing a self-organizing behavior wi...
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Self-organizing principles can address the growing complexity and the huge challenge of management and efficient utilization of adaptive many-core architectures. Fundamental for realizing a self-organizing behavior within such architectures is a dedicated monitoring infrastructure that provides the essential information about the system status and system behavior for realizing the basic property of self-awareness. This paper therefore proposes a flexible, hierarchical and scalable monitoring infrastructure for self-organizing, adaptive many-core architectures. The employed basic monitoring unit in the bottom monitoring layer performs data aggregation and filtering and reduces the amount of data that must be processed in higher monitoring layers. The middle layer performs first data analysis and is further responsible for hiding the heterogeneity of the underlying hardware configuration to the topmost monitoring layer. The latter is finally responsible for detecting changes in the system behavior and realizing self-awareness. The proposed monitoring infrastructure was evaluated entirely using a simulation framework. Results show that the infrastructure is able of detecting changes in the system behavior of an entire many-core system causing only a minor system disturbance. Further, the prototypical implementation of the basic monitoring unit proved that it can be realized very efficiently in hardware.
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