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Simple algorithms can be analytically characterized, but such analysis is questionable or even impossible for more complicated algorithms, such as Model Predictive Control (MPC). Instead, Monte Carlo Arithmetic (MCA) enables statistical experimentation with an algorithm during runtime for detection and mitigation of numerical anomalies. Previous studies of MCA have been limited to software floating...
The benchmark of pricing a European option via Monte Carlo simulation is commonly used in financial engineering for evaluating the performance of new computational techniques and to tune the parameters of the Monte Carlo simulation for improved convergence. This paper presents a comparison of different FPGA implementations of the European option benchmark against other implementations using GPUs,...
Many computer simulations require large quantities of uncorrelated random numbers to be generated quickly. Examples include all forms of Monte Carlo simulation, generating phase screens to simulate the effects of atmospheric turbulence and the simulation of electrical noise in sensors. A flexible way to generate random numbers of arbitrary distribution is to modify the distribution of a source of...
Process variations of deep sub-micron technologies have created significant timing uncertainty. This generates the need for a new variability-aware physical synthesis tool for Field-Programmable Gate-Arrays (FPGAs). Ideally, variability-aware tools should be able to perform both timing variability estimation during the synthesis and timing variability analysis after the synthesis. Statistical static...
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