Abner Correa Barros
João Paulo Barbosa
ABSTRACT
Recently, the manufactures of supercomputers have made use of FPGAs to accelerate scientific applications [16][17]. Traditionally, the FPGAs were used only on non-scientific applications. The main reasons for this fact are: the floating-point computation complexity; the FPGA logic cells are not sufficient for the scientific cores implementation; the cores complexity prevents them to operate on high frequencies.
Nowadays, the increase of specialized blocks availability in complex operations, as sum and multiplier blocks, implemented directly in FPGA and, the increase of internal RAM blocks (BRAMs) have made possible high performance systems that use FPGA as a processing element for scientific computation [2].
These devices are used as co-processors that execute intensive computation. The emphasis of these architectures is the exploration of parallelism present on scientific computation operations and data reuse.
In major of these applications, the scientific computation uses, in general, operations of big floating-point dense matrices, which are normally operated by MACs.
In this work, we describe the architecture of an accumulative multiplier (MAC) in double precision floating-point, according to IEEE-754 standard and we propose the architecture of a multiplier of matrices that uses developed instances of the MACs and explores the reuse of data through the use of the BRAMs (Blocks of RAM internal to the FPGAs) of a Xilinx Virtex 4 LX200 FPGA. The synthesis results showed that the implemented MAC could reach a performance of 4GFLOPs.
- Ling Zhuo, Viktor K. Prasanna, Scalable and Modular Algorithms for Floating-Point Matrix Multiplication on Reconfigurable Computing Systems, IEEE Transactions on Parallel and Distributed Systems (TPDS), Vol. 18, No. 4, pp. 433--448, April 2007. Google Scholar
Digital Library
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- L. Zhuo and V. K. Prasanna. Scalable and Modular Algorithms for Floating-Point matrix Multiplication on FPGAs. In Proc. of the 18th International Parallel & Distributed Processing Simposium, New Mexico, USA, April 2004.Google Scholar
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- K. D. Underwood and K. S. Hemmert. Closing the Gap:CPU and FPGA Trends in Sustainable Floating-Point BLAS Performance. In Proc. IEEE Symp. Field-Programmable Custom Computing Machines (FCCM'04), April 2004. Google ScholarDigital Library
- L. Zhuo and V. K. Prasanna. Scalable and Modular Algorithms for Floating-Point Matrix Multiplication on FPGAs. In Proc. 18th Int'l Parallel & Distributed Processing Symp. (IPDPS'04), New Mexico, USA, April 2004.Google Scholar
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Index Terms
- Implementation of a double-precision multiplier accumulator with exception treatment to a dense matrix multiplier module in FPGA
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