FPGA-based reconfigurable systems allow the online adaptation to dynamically changing runtime requirements. However, the reliability of modern FPGAs is threatened by latent defects and aging effects. Hence, it is mand...
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FPGA-based reconfigurable systems allow the online adaptation to dynamically changing runtime requirements. However, the reliability of modern FPGAs is threatened by latent defects and aging effects. Hence, it is mandatory to ensure the reliable operation of the FPGA's reconfigurable fabric. This can be achieved by periodic or on-demand online testing. In this paper, a system-integrated, transparent structural online test method for runtime reconfigurable systems is proposed. The required tests are scheduled like functional workloads, and thorough optimizations of the test overhead reduce the performance impact. The proposed scheme has been implemented on a reconfigurable system. The results demonstrate that thorough testing of the reconfigurable fabric can be achieved at negligible performance impact on the application.
Industrial image processing tasks, especially in the domain of optical metrology, are becoming more and more complex. While in the recent years standard PC components were sufficient to fulfill the requirements, speci...
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Industrial image processing tasks, especially in the domain of optical metrology, are becoming more and more complex. While in the recent years standard PC components were sufficient to fulfill the requirements, special architectures have to be used to build up high speed image processing systems today. For example for adaptive optical systems in large scale telescopes, the latency between capturing an image and steering the mirrors is critical for the quality of the resulting images. Commonly, the applied image processing algorithms consist of several tasks with different granularities and complexities. Therefore, we combined the advantages of multicore CPUs, GPUs, and FPGAs to build up a heterogeneous image processing pipeline for adaptive optical systems. Each used architecture is well-suited to solve a particular task efficiently. With the developed pipeline it is possible to achieve a high throughput and to reduce the latency of the whole steering system significantly.
The efficient realization of self-organizing systems based on 2D stencil code applications, like our developed Marching Pixel algorithms, is a great challenge. They are data-intensive and also computational-intensive,...
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The efficient realization of self-organizing systems based on 2D stencil code applications, like our developed Marching Pixel algorithms, is a great challenge. They are data-intensive and also computational-intensive, because often a high number of iterations is required. FPGAs are predestined for the realization of these algorithms. They are very flexible, allow a scalable parallel processing and have a moderate power consumption, even in high-performance versions. Therefore, FPGAs are highly qualified to make these applications also real-time capable. Our goal was to implement an efficient parameterizable buffering and parallel processing scheme for such operations in FPGAs, to process them as fast as possible. We developed a generic VHDL template which allows a scalable parallelization and pipelining of 2D stencil code applications in relation to application and hardware constraints.
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.
Mobility and multi-homing are closely related and mainly issues of the used IP addressing scheme. Novel addressing schemes that separate node identification and location are promising concepts to tackle the named issu...
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Mobility and multi-homing are closely related and mainly issues of the used IP addressing scheme. Novel addressing schemes that separate node identification and location are promising concepts to tackle the named issues. However, current approaches that make use of the address separation concept offer, among others, insufficient capabilities to select one or multiple network interfaces for a certain communication process, e.g. to enable load balancing. Therefore, we propose a novel approach called Node-oriented Internet Protocol (NIP). NIP adapts the address separation concept, introduces a novel three-tuple addressing scheme that adds an interface ID and enables network nodes to select sets of network interfaces to be used for communication sessions. Our work includes basic concepts for mapping system, schemes for packet forwarding, mechanisms for mobility and multi-homing support and strategies to ensure backwards compatibility. Building on that, the NIP concept enhances multi-homing and mobility capabilites with new functionalities in comparison to known approaches, especially referred to interface selection and bandwidth aggregation.
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.
Competition for shared memory resources on multiprocessors is the most dominant cause for slowing down applications and makes their performance varies unpredictably. It exacerbates the need for Quality of Service (QoS...
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This work presents a recognition system for epileptiform abnormalities based on electroencephalogram (EEG) analysis. The proposed system combines a Support Vector Machine classifier automatically trained by an impleme...
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Virtualization and broadband Internet connections enabled existing technologies to form up under the nebulous term cloud. Cloud computing promises near infinite scalability and cost reduction by pay per use agreements...
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Virtualization and broadband Internet connections enabled existing technologies to form up under the nebulous term cloud. Cloud computing promises near infinite scalability and cost reduction by pay per use agreements. However for data outsourced to existing cloud solutions - which can be considered synonymous with unknown locations and potentially hostile environments - the security protection objectives can not be guaranteed. We present an approach that enables users to benefit from cloud computing and retain data sovereignty.
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