Academic literature on the topic 'Reconfigurable Hardware Architecture'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Reconfigurable Hardware Architecture.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Reconfigurable Hardware Architecture"
Thomas, Alexander, Michael Rückauer, and Jürgen Becker. "HoneyComb: An Application-Driven Online Adaptive Reconfigurable Hardware Architecture." International Journal of Reconfigurable Computing 2012 (2012): 1–17. http://dx.doi.org/10.1155/2012/832531.
Full textSiddiqui, Ali Shuja, Yutian Gui, and Fareena Saqib. "Secure Boot for Reconfigurable Architectures." Cryptography 4, no. 4 (September 25, 2020): 26. http://dx.doi.org/10.3390/cryptography4040026.
Full textFabiani, Erwan. "Experiencing a Problem-Based Learning Approach for Teaching Reconfigurable Architecture Design." International Journal of Reconfigurable Computing 2009 (2009): 1–11. http://dx.doi.org/10.1155/2009/923415.
Full textPionteck, Thilo, Roman Koch, Carsten Albrecht, and Erik Maehle. "A Design Technique for Adapting Number and Boundaries of Reconfigurable Modules at Runtime." International Journal of Reconfigurable Computing 2009 (2009): 1–10. http://dx.doi.org/10.1155/2009/942930.
Full textGöhringer, Diana, Thomas Perschke, Michael Hübner, and Jürgen Becker. "A Taxonomy of Reconfigurable Single-/Multiprocessor Systems-on-Chip." International Journal of Reconfigurable Computing 2009 (2009): 1–11. http://dx.doi.org/10.1155/2009/395018.
Full textWijtvliet, Mark, Henk Corporaal, and Akash Kumar. "CGRA-EAM—Rapid Energy and Area Estimation for Coarse-grained Reconfigurable Architectures." ACM Transactions on Reconfigurable Technology and Systems 14, no. 4 (December 31, 2021): 1–28. http://dx.doi.org/10.1145/3468874.
Full textVoss, Nils, Bastiaan Kwaadgras, Oskar Mencer, Wayne Luk, and Georgi Gaydadjiev. "On Predictable Reconfigurable System Design." ACM Transactions on Architecture and Code Optimization 18, no. 2 (March 2021): 1–28. http://dx.doi.org/10.1145/3436995.
Full textCraven, Stephen, and Peter Athanas. "Dynamic Hardware Development." International Journal of Reconfigurable Computing 2008 (2008): 1–10. http://dx.doi.org/10.1155/2008/901328.
Full textNAKANO, KOJI. "A BIBLIOGRAPHY OF PUBLISHED PAPERS ON DYNAMICALLY RECONFIGURABLE ARCHITECTURES." Parallel Processing Letters 05, no. 01 (March 1995): 111–24. http://dx.doi.org/10.1142/s0129626495000102.
Full textPurohit, Gaurav, Kota Solomon Raju, and Vinod Kumar Chaubey. "XOR-FREE Implementation of Convolutional Encoder for Reconfigurable Hardware." International Journal of Reconfigurable Computing 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/9128683.
Full textDissertations / Theses on the topic "Reconfigurable Hardware Architecture"
Gelb, Benjamin S. "A timeshared, runtime reconfigurable hardware co-processing architecture." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53147.
Full textIncludes bibliographical references (leaves 73-74).
The constant desire for increased performance in microprocessor systems has led to the need for specialized hardware cores to accelerate specific computational tasks. In this thesis, we explore the potential of using FPGA partial reconfiguration to create a platform for customized hardware cores that may be loaded on demand, at runtime, and replaced when not in use. We implement two new software tools, bitparse and bitrender, to demonstrate the bitstream relocation technique. Further, we present a functional microprocessor system coupled with a runtime reprogramable peripheral synthesized on a Xilinx Virtex-5 FPGA and discuss its performance implications.
by Benjamin S. Gelb.
M.Eng.
Peterkin, Raymond. "A reconfigurable hardware architecture for VPN MPLS based services." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27283.
Full textDiniz, Claudio Machado. "Dedicated and reconfigurable hardware accelerators for high efficiency video coding standard." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/118394.
Full textThe demand for ultra-high resolution video (beyond 1920x1080 pixels) led to the need of developing new and more efficient video coding standards to provide high compression efficiency. The High Efficiency Video Coding (HEVC) standard, published in 2013, reaches double compression efficiency (or 50% reduction in size of coded video) compared to the most efficient video coding standard at that time, and most used in the market, the H.264/AVC (Advanced Video Coding) standard. HEVC reaches this result at the cost of high computational effort of the tools included in the encoder and decoder. The increased computational effort of HEVC standard and the power limitations of current silicon fabrication technologies makes it essential to develop hardware accelerators for compute-intensive computational kernels of HEVC application. Hardware accelerators provide higher performance and energy efficiency than general purpose processors for specific applications. An HEVC application analysis conducted in this work identified the most compute-intensive kernels of HEVC, namely the Fractional-pixel Interpolation Filter, the Deblocking Filter and the Sum of Absolute Differences calculation. A run-time analysis on Interpolation Filter indicates a great potential of power/energy saving by adapting the hardware accelerator to the varying workload. This thesis introduces new contributions in the field of dedicated and reconfigurable hardware accelerators for HEVC standard. Dedicated hardware accelerators for the Fractional Pixel Interpolation Filter, the Deblocking Filter and the Sum of Absolute Differences calculation are herein proposed, designed and evaluated. The interpolation filter hardware architecture achieves throughput similar to the state of the art, while reducing hardware area by 50%. Our deblocking filter hardware architecture also achieves similar throughput compared to state of the art with a 5X to 6X reduction in gate count and 3X reduction in power dissipation. The thesis also does a new comparative analysis of Sum of Absolute Differences processing elements, in which various architecture design alternatives with different area, performance and power results were introduced. A novel reconfigurable interpolation filter hardware architecture for HEVC standard was developed, and it provides 57% design-time area reduction and run-time power/energy adaptation in a picture-by-picture basis, compared to the state-of-the-art. Additionally a run-time accelerator binding scheme is proposed for tile-based mixed-grained reconfigurable architectures, which reduces the communication overhead, compared to first-fit strategy with datapath reusing scheme, by up to 44% (23% on average) for different number of tiles and internal tile organizations. This run-time accelerator binding scheme is aware of the underlying architecture to bind datapaths in an efficient way, to avoid and minimize inter-tile communications. The new dedicated and reconfigurable hardware accelerators and techniques proposed in this thesis enable next-generation video coding standard implementations beyond HEVC with improved area, performance, and power efficiency.
Kung, Ling-Pei 1961. "Obtaining performance and programmability using reconfigurable hardware for media processing." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/61855.
Full textIncludes bibliographical references (p. 127-132).
An imperative requirement in the design of a reconfigurable computing system or in the development of a new application on such a system is performance gains. However, such developments suffer from long-and-difficult programming process, hard-to-predict performance gains, and limited scope of applications. To address these problems, we need to understand reconfigurable hardware's capabilities and limitations, its performance advantages and disadvantages, re-think reconfigurable system architectures, and develop new tools to explore its utility. We begin by examining performance contributors at the system level. We identify those from general-purpose and those from dedicated components. We propose an architecture by integrating reconfigurable hardware within the general-purpose framework. This is to avoid and minimize dedicated hardware and organization for programmability. We analyze reconfigurable logic architectures and their performance limitations. This analysis leads to a theory that reconfigurable logic can never be clocked faster than a fixed-logic design based on the same fabrication technology. Though highly unpredictable, we can obtain a quick upper bound estimate on the clock speed based on a few parameters. We also analyze microprocessor architectures and establish an analytical performance model. We use this model to estimate performance bounds using very little information on task properties. These bounds help us to detect potential memory-bound tasks. For a compute-bound task, we compare its performance upper bound with the upper bound on reconfigurable clock speed to further rule out unlikely speedup candidates.
(cont.) These performance estimates require very few parameters, and can be quickly obtained without writing software or hardware codes. They can be integrated with design tools as front end tools to explore speedup opportunities without costly trials. We believe this will broaden the applicability of reconfigurable computing.
by Ling-Pei Kung.
Ph.D.
Balasubramanian, Karthikeyan. "Reconfigurable System-on-Chip Architecture for Neural Signal Processing." Diss., Temple University Libraries, 2011. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/144255.
Full textPh.D.
Analyzing the brain's behavior in terms of its neuronal activity is the fundamental purpose of Brain-Machine Interfaces (BMIs). Neuronal activity is often assumed to be encoded in the rate of neuronal action potential spikes. Successful performance of a BMI system is tied to the efficiency of its individual processing elements such as spike detection, sorting and decoding. To achieve reliable operation, BMIs are equipped with hundreds of electrodes at the neural interface. While a single electrode/tetrode communicates with up to four neurons at a given instant of time, a typical interface communicates with an ensemble of hundreds or even thousands of neurons. However, translation of these signals (data) into usable information for real-time BMIs is bottlenecked due to the lack of efficient real-time algorithms and real-time hardware that can handle massively parallel channels of neural data. The research presented here addresses this issue by developing real-time neural processing algorithms that can be implemented in reconfigurable hardware and thus, can be scaled to handle thousands of channels in parallel. The developed reconfigurable system serves as an evaluation platform for investigating the fundamental design tradeoffs in allocating finite hardware resources for a reliable BMI. In this work, the generic architectural layout needed to process neural signals in a massive scale is discussed. A System-on-Chip design with embedded system architecture is presented for FPGA hardware realization that features (a) scalability (b) reconfigurability, and (c) real-time operability. A prototype design incorporating a dual processor system and essential neural signal processing routines such as real-time spike detection and sorting is presented. Two kinds of spike detectors, a simple threshold-based and non-linear energy operator-based, were implemented. To achieve real-time spike sorting, a fuzzy logic-based spike sorter was developed and synthesized in the hardware. Furthermore, a real-time kernel to monitor the high-level interactions of the system was implemented. The entire system was realized in a platform FPGA (Xilinx Virtex-5 LX110T). The system was tested using extracellular neural recordings from three different animals, a owl monkey, a macaque and a rat. Operational performance of the system is demonstrated for a 300 channel neural interface. Scaling the system to 900 channels is trivial.
Temple University--Theses
Lomonaco, Michael John. "CRYPTARRAY A SCALABLE AND RECONFIGURABLE ARCHITECTURE FOR CRYPTOGRAPHIC APPLICATIONS." Master's thesis, University of Central Florida, 2004. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4394.
Full textM.S.
Department of Electrical and Computer Engineering
Engineering and Computer Science
Electrical and Computer Engineering
Avakian, Annie. "Reducing Cache Access Time in Multicore Architectures Using Hardware and Software Techniques." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1335461322.
Full textSilva, Antonio Carlos Fernandes da. "ChipCflow: tool for convert C code in a static dataflow architecture in reconfigurable hardware." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-30062015-141638/.
Full textExiste uma crescente busca por softwares e arquiteturas alternativas. Essa busca acontece pois houveram avanços na tecnologia do hardware, e estes avanços devem ser complementados por inovações nas metodologias de projetos, testes e verificação para que haja um uso eficaz da tecnologia. Os software e arquiteturas alternativas, geralmente são modelos que exploram o paralelismo das aplicações, ao contrário do modelo de Von Neumann. Dentre as arquiteturas alternativas de alto desempenho, tem-se a arquitetura a fluxo de dados. Nesse tipo de arquitetura, o processo de execução de programas é determinado pela disponibilidade dos dados, logo o paralelismo está embutido na própria natureza do sistema. O modelo a fluxo de dados possui a vantagem de expressar o paralelismo de maneira intrínseca, eliminando a necessidade do programador explicitar em seu código os trechos onde deve haver paralelismo. As arquiteturas a fluxo de dados voltaram a ser uma área de pesquisa devido aos avanços do hardware, em particular, os avanços da Computação Reconfigurável e dos Field Programmable Gate Arrays (FPGAs).Nesta tese é descrita uma ferramenta de conversão de código que visa a geração de aplicações utilizando uma arquitetura a fluxo de dados estática. Também é descrito o projeto ChipCflow, cuja ferramenta de conversão de código, descrita nesta tese, é parte integrante. A especificação do algoritmo a ser convertido é feita em linguagem C e convertida para uma linguagem de descrição de hardware, respeitando o modelo proposto pelo ChipCflow. Os resultados alcançados visam a prova de conceito da conversão de código de uma linguagem de alto nível para uma arquitetura a fluxo de dados a ser configurada em FPGA.
Robinson, Kylan Thomas. "An integrated development environment for the design and simulation of medium-grain reconfigurable hardware." Pullman, Wash. : Washington State University, 2010. http://www.dissertations.wsu.edu/Thesis/Spring2010/k_robinson_041510.pdf.
Full textTitle from PDF title page (viewed on June 22, 2010). "School of Electrical Engineering and Computer Science." Includes bibliographical references (p. 75-76).
Das, Satyajit. "Architecture and Programming Model Support for Reconfigurable Accelerators in Multi-Core Embedded Systems." Thesis, Lorient, 2018. http://www.theses.fr/2018LORIS490/document.
Full textEmerging trends in embedded systems and applications need high throughput and low power consumption. Due to the increasing demand for low power computing and diminishing returns from technology scaling, industry and academia are turning with renewed interest toward energy efficient hardware accelerators. The main drawback of hardware accelerators is that they are not programmable. Therefore, their utilization can be low is they perform one specific function and increasing the number of the accelerators in a system on chip (SoC) causes scalability issues. Programmable accelerators provide flexibility and solve the scalability issues. Coarse-Grained Reconfigurable Array (CGRA) architecture consisting of several processing elements with word level granularity is a promising choice for programmable accelerator. Inspired by the promising characteristics of programmable accelerators, potentials of CGRAs in near threshold computing platforms are studied and an end-to-end CGRA research framework is developed in this thesis. The major contributions of this framework are: CGRA design, implementation, integration in a computing system, and compilation for CGRA. First, the design and implementation of a CGRA named Integrated Programmable Array (IPA) is presented. Next, the problem of mapping applications with control and data flow onto CGRA is formulated. From this formulation, several efficient algorithms are developed using internal resources of a CGRA, with a vision for low power acceleration. The algorithms are integrated into an automated compilation flow. Finally, the IPA accelerator is augmented in PULP - a Parallel Ultra-Low-Power Processing-Platform to explore heterogeneous computing
Books on the topic "Reconfigurable Hardware Architecture"
N, Mahapatra Rabi, ed. Design of low-power coarse-grained reconfigurable architectures. Boca Raton, FL: CRC Press, 2011.
Find full textNedjah, Nadia, and Chao Wang. Reconfigurable and Adaptive Computing: Theory and Applications. Taylor & Francis Group, 2018.
Find full textNedjah, Nadia, and Chao Wang. Reconfigurable and Adaptive Computing: Theory and Applications. Taylor & Francis Group, 2018.
Find full textReconfigurable and Adaptive Computing: Theory and Applications. Taylor & Francis Group, 2018.
Find full textReconfigurable and Adaptive Computing: Theory and Applications. Taylor & Francis Group, 2018.
Find full textNedjah, Nadia, and Chao Wang. Reconfigurable and Adaptive Computing: Theory and Applications. Taylor & Francis Group, 2018.
Find full textMahapatra, Rabi N., and Yoonjin Kim. Design of Low-Power Coarse-Grained Reconfigurable Architectures. Taylor & Francis Group, 2010.
Find full textMahapatra, Rabi N., and Yoonjin Kim. Design of Low-Power Coarse-Grained Reconfigurable Architectures. Taylor & Francis Group, 2017.
Find full textMahapatra, Rabi N., and Yoonjin Kim. Design of Low-Power Coarse-Grained Reconfigurable Architectures. Taylor & Francis Group, 2010.
Find full textMahapatra, Rabi N., and Yoonjin Kim. Design of Low-Power Coarse-Grained Reconfigurable Architectures. Taylor & Francis Group, 2010.
Find full textBook chapters on the topic "Reconfigurable Hardware Architecture"
Liu, Leibo, Bo Wang, and Shaojun Wei. "Hardware Architecture of Reconfigurable Cryptographic Processors." In Reconfigurable Cryptographic Processor, 133–67. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8899-5_3.
Full textThomas, Alexander, and Jürgen Becker. "Development and Synthesis of Adaptive Multi-grained Reconfigurable Hardware Architecture for Dynamic Function Patterns." In Dynamically Reconfigurable Systems, 3–24. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-3485-4_1.
Full textGoodman, James, and Anantha Chandrakasan. "An Energy Efficient Reconfigurable Public-Key Cryptography Processor Architecture." In Cryptographic Hardware and Embedded Systems — CHES 2000, 175–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44499-8_13.
Full textChen, Mengdong, Xiujiang Ren, and Xianghui Xie. "An Efficient Rule Processing Architecture Based on Reconfigurable Hardware." In Communications in Computer and Information Science, 22–33. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1850-8_3.
Full textBaidya, Paresh, Rourab Paul, and Suman Sau. "High-Speed Loop Unrolled Grain Architecture in Reconfigurable Hardware." In Advances in Intelligent Systems and Computing, 165–73. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1483-8_15.
Full textNakada, Hiroshi, Kiyoshi Oguri, Norbert Imlig, Minoru Inamori, Ryusuke Koniski, Hideyuki Ito, Kouichi Nagami, and Tsunemichi Shiozawa. "Plastic cell architecture: A dynamically reconfigurable hardware-based computer." In Lecture Notes in Computer Science, 679–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/bfb0097953.
Full textNedjah, Nadia, and Luiza de Macedo Mourelle. "Reconfigurable Hardware Architecture for Compact and Efficient Stochastic Neuron." In Artificial Neural Nets Problem Solving Methods, 17–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44869-1_3.
Full textKnieper, Tobias, Paul Kaufmann, Kyrre Glette, Marco Platzner, and Jim Torresen. "Coping with Resource Fluctuations: The Run-time Reconfigurable Functional Unit Row Classifier Architecture." In Evolvable Systems: From Biology to Hardware, 250–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15323-5_22.
Full textAhn, Seong-Yong, Jun-Yong Kim, and Jeong-A. Lee. "Heuristic Algorithm for Reducing Mapping Sets of Hardware-Software Partitioning in Reconfigurable System." In Advances in Computer Systems Architecture, 102–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30102-8_9.
Full textWeng, Sheng-Kai, Chien-Min Ou, and Wen-Jyi Hwang. "VLSI Architecture for Fast Memetic Vector Quantizer Design on Reconfigurable Hardware." In Algorithms and Architectures for Parallel Processing, 513–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03095-6_49.
Full textConference papers on the topic "Reconfigurable Hardware Architecture"
Ahmad, B., A. T. Erdogan, and S. Khawam. "Architecture of a Dynamically Reconfigurable NoC for Adaptive Reconfigurable MPSoC." In First NASA/ESA Conference on Adaptive Hardware and Systems (AHS'06). IEEE, 2006. http://dx.doi.org/10.1109/ahs.2006.25.
Full textRodriguez, Vladimir, Jose F. Martinez, Jesus A. Carrasco, Manuel S. Lazo, Rene Cumplido, and Claudia Feregrino Uribe. "A hardware architecture for filtering irreducible testors." In 2014 International Conference on Reconfigurable Computing and FPGAs (ReConFig14). IEEE, 2014. http://dx.doi.org/10.1109/reconfig.2014.7032526.
Full textWang, Ying, Jian Yan, Xuegong Zhou, Lingli Wang, Wayne Luk, Chenglian Peng, and Jiarong Tong. "A partially reconfigurable architecture supporting hardware threads." In 2012 International Conference on Field-Programmable Technology (FPT 2012). IEEE, 2012. http://dx.doi.org/10.1109/fpt.2012.6412147.
Full textJung, Yong-kyu. "A Hardware/Software Co-reconfigurable Multimedia Architecture." In 2006 IEEE/ACM/IFIP Workshop on Embedded Systems for Real Time Multimedia. IEEE, 2006. http://dx.doi.org/10.1109/estmed.2006.321277.
Full textLi, Zhijing, Yuwei Ye, Stephen Neuendorffer, and Adrian Sampson. "Compiler-Driven Simulation of Reconfigurable Hardware Accelerators." In 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA). IEEE, 2022. http://dx.doi.org/10.1109/hpca53966.2022.00052.
Full textTasdizen, O., H. Kukner, A. Akin, and I. Hamzaoglu. "A high performance reconfigurable Motion Estimation hardware architecture." In 2009 Design, Automation & Test in Europe Conference & Exhibition (DATE'09). IEEE, 2009. http://dx.doi.org/10.1109/date.2009.5090787.
Full textDias, Mauricio A., and Fernando S. Osorio. "Reconfigurable Hardware Architecture for Vision-Based Driving Systems." In 2015 12th Latin American Robotics Symposium (LARS) and 2015 3rd Brazilian Symposium on Robotics (LARS-SBR). IEEE, 2015. http://dx.doi.org/10.1109/lars-sbr.2015.65.
Full textPedraza, Cesar, Emilio Castillo, Javier Castillo, Cristobal Camarero, Jose L. Bosque, Jose I. Martinez, and Rafael Menendez. "Cluster architecture based on low cost reconfigurable hardware." In 2008 International Conference on Field Programmable Logic and Applications (FPL). IEEE, 2008. http://dx.doi.org/10.1109/fpl.2008.4630017.
Full textKhalil, Kasem, Omar Eldash, Bappaditya Dey, Ashok Kumar, and Magdy Bayoumi. "A Novel Reconfigurable Hardware Architecture of Neural Network." In 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2019. http://dx.doi.org/10.1109/mwscas.2019.8884809.
Full textTu, Fengbin, Shouyi Yin, Peng Ouyang, Leibo Liu, and Shaojun Wei. "RNA: A Reconfigurable Architecture for Hardware Neural Acceleration." In Design, Automation and Test in Europe. New Jersey: IEEE Conference Publications, 2015. http://dx.doi.org/10.7873/date.2015.0414.
Full text