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FPGAs normally have numerous independent memory banks that can be accessed simultaneously, potentially offering a very large memory bandwidth. Adopting a suitable application-based memory partitioning strategy is thus vital to take full advantage of the memory architecture. In addition to improving the potential memory bandwidth, partitioning also affects the area complexity of the generated system...
FPGAs are commonly used in high performance computing applications, often in the form of streaming systems which exploit parallelism of algorithms along pipelined kernels. While such applications have traditionally been designed at the Register Transfer Level (RTL), the increasing complexity in terms of FPGA resource usage, arithmetic logic and dataflow is causing the time taken for RTL programming...
This work demonstrates the capabilities of a high-level synthesis tool-chain that allows the compilation of higher order functional programs to gate-level hardware descriptions. Higher order programming allows functions to take functions as parameters. In a hardware context, the latency-insensitive interfaces generated between compiled modules enable late-binding with libraries of pre-existing functions...
This paper presents a novel parallel architecture for accelerating quadrature methods used for pricing complex multi-dimensional options, such as discrete barrier, Bermudan and American options. We explore different designs of the quadrature evaluation core including optimized pipelined hardware designs in reconfigurable logic and a compute unified device architecture (CUDA)-based graphics processing...
This paper presents a methodology and the results of implementing Monte-Carlo financial simulations in reconfigurable devices. Five different Monte-Carlo simulations are explored, including log-normal price movements, correlated asset value-at-risk calculation, and price movements under the GARCH model. Our results show that hardware implementations from our approach on a Xilinx Virtex-4 XC4VSX55...
This paper presents a framework for the acceleration of Monte-Carlo simulations using reconfigurable hardware. Discrete-time random walk simulations are widely used in the financial computation to calculate derivative prices and evaluate portfolio risk, but increases in model complexity and tighter time constraints now require large computer farms to meet operational demands. We present a model for...
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