Field Programmable Gate Array (FPGA): A Tool for Improving Parallel Applications (original) (raw)
Related papers
Application of Field Programmable Gate Array (FPGA) To Digital Signal Processing(DSP
This work shows how one parallel technology Field Programmable Gate Array (FPGA) can be applied to digital signal processing problem to increase computational speed. The best algorithm for solving Digital Signal Processing Applications; Fast Fourier Transform (FFT) algorithm has shown significant speed improvement when implemented on a FPGA. The design methodology, the design tools for implementing DSP functions in FPGAs is discussed e.g. System Generator from Xilinx, Impulse C programming model etc. FPGA design in compares with other technolog) is envisaged. In this research work FPGA typically exploits parallelism because FPGA is a parallel device. With the use of simulation tool, Impulse Codeveloper (Impulse C), of FPGA platform on FFT algorithm, graphical tools that provide initial estimates of algorithm throughput such as loop latencies and pipeline effective rates are generated. Using such tools, you can interactively change optimization options or iteratively modify and recompile C code to obtain higher performance.
Parallel Computing with FPGAs - Concepts and Applications
The Mini-Symposium "Parallel computing with FPGAs" aimed at exploring the many ways in which field programmable gate arrays can be arranged into high-performance computing blocks. Examples include high-speed operations obtained by sheer parallelism, numerical algorithms mapped into hardware, co-processing time critical sections and the development of powerful programming environments for hardware software co-design.
Review of parallel computing methods and tools for FPGA
Parallel computing is emerging as an important area of research in computer architectures and software systems. Many algorithms can be greatly accelerated using parallel computing techniques. Specialized parallel computer architectures are used for accelerating specific tasks. High-Energy Physics Experiments measuring systems often uses FPGAs for fine-grained computation. FPGA combines many benefits of both software and ASIC implementations. Like software, the mapped circuit is flexible, and can be reconfigured over the lifetime of the system. FPGAs therefore have the potential to achieve far greater performance than software as a result of bypassing the fetch-decode-execute operations of traditional processors, and possibly exploiting a greater level of parallelism. Creating parallel programs implemented in FPGAs is not trivial. This paper presents existing methods and tools for fine-grained computation implemented in FPGA using High Level Programming Languages.
FPGA Based High Performance Computing
Current high performance computing (HPC) applications are found in many consumer, industrial and research fields. From web searches to auto crash simulations to weather predictions, these applications require large amounts of power by the compute farms and supercomputers required to run them. The demand for more and faster computation continues to increase along with an even sharper increase in the cost of the power required to operate and cool these installations. The ability of standard processor based systems to address these needs has declined in both speed of computation and in power consumption over the past few years. This paper presents a new method of computation based upon programmable logic as represented by Field Programmable Gate Arrays (FPGAs) that addresses these needs in a manner requiring only minimal changes to the current software design environment.
Review of parallel computing methods and tools for FPGA technology
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2013, 2013
Parallel computing is emerging as an important area of research in computer architectures and software systems. Many algorithms can be greatly accelerated using parallel computing techniques. Specialized parallel computer architectures are used for accelerating specific tasks. High-Energy Physics Experiments measuring systems often use FPGAs for fine-grained computation. FPGA combines many benefits of both software and ASIC implementations. Like software, the mapped circuit is flexible, and can be reconfigured over the lifetime of the system. FPGAs therefore have the potential to achieve far greater performance than software as a result of bypassing the fetch-decode-execute operations of traditional processors, and possibly exploiting a greater level of parallelism. Creating parallel programs implemented in FPGAs is not trivial. This paper presents existing methods and tools for fine-grained computation implemented in FPGA using Behavioral Description and High Level Programming Languages.
IJERT-Simulation & Synthesis of FPGA Based & Resource Efficient Matrix Coprocessor Architecture
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/simulation-synthesis-of-fpga-based-resource-efficient-matrix-coprocessor-architecture https://www.ijert.org/research/simulation-synthesis-of-fpga-based-resource-efficient-matrix-coprocessor-architecture-IJERTV3IS10602.pdf Due to fast routing and logic resources, embedded multipliers, very high gate densities, block RAM memory modules, and embedded soft processors, modern FPGAs offer an excellent platform for the implementation of efficient Field Programmable Systems on Chip. The latter are largely required in a wide range of applications, like telecommunication, wireless, networking, video, signal processing, robotics and digital control. All the above applications are characterized by an intensive computation of matrix operations like the matrix multiplication. Therefore, the introduction of a coprocessor to support the computation of matrix multiplications alongside a general purpose processor system can be a good solution to reduce the computational time & Resources. However, in order to get the most of their potential performance, FPGAs need to be programmed at the hardware low level and not the application level. This process needs a considerable hardware knowledge, which means that FPGA programming is still reserved to the specialist. The recent explosion in FPGAs resource densities and performance, together with their inherent reprogram ability feature, makes them very attractive as high performance, flexible, implementation platforms for these operations. In this present work a novel architecture has been developed for large matrix multiplication. For high performance applications, this operation will minimize the hardware resources. For this, we use a parallel architecture for the multiplication of two matrices using Field programmable Gate Array (FPGA).
… , Signal Processing and …, 2009
Algorithms used in signal and image processing applications are computationally intensive. For optimized hardware realization of such algorithms with efficient utilization of available resources, an in-depth knowledge of the targeted field programmable gate array (FPGA) technology is required. This paper presents an overview of the architectures and technologies used in modern FPGAs. A case study of most popular and widely used state-of-the-art commercial FPGA technologies from Xilinx and Altera is also presented. Three-Dimensional (3D)-FPGA architecture is also discussed.
Simulation & Synthesis of FPGA Based & Resource Efficient Matrix Coprocessor Architecture
2014
Due to fast routing and logic resources, embedded multipliers, very high gate densities, block RAM memory modules, and embedded soft processors, modern FPGAs offer an excellent platform for the implementation of efficient Field Programmable Systems on Chip. The latter are largely required in a wide range of applications, like telecommunication, wireless, networking, video, signal processing, robotics and digital control. All the above applications are characterized by an intensive computation of matrix operations like the matrix multiplication. Therefore, the introduction of a coprocessor to support the computation of matrix multiplications alongside a general purpose processor system can be a good solution to reduce the computational time & Resources. However, in order to get the most of their potential performance, FPGAs need to be programmed at the hardware low level and not the application level. This process needs a considerable hardware knowledge, which means that FPGA programming is still reserved to the specialist. The recent explosion in FPGAs resource densities and performance, together with their inherent reprogram ability feature, makes them very attractive as high performance, flexible, implementation platforms for these operations. In this present work a novel architecture has been developed for large matrix multiplication. For high performance applications, this operation will minimize the hardware resources. For this, we use a parallel architecture for the multiplication of two matrices using Field programmable Gate Array (FPGA).