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PPKE ITK PhD and MPhil Thesis Classes

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3.5 Results 83<br />

arithmetic unit requires only 774 slices from the FPGA, the 40 bit floating-point<br />

(which has only 29 bit mantissa) uses 11718 slices.<br />

1.00E-02<br />

1.00E-03<br />

Error<br />

1.00E-04<br />

1.00E-05<br />

1.00E+02 1.00E+03 Resolution 1.00E+04 1.00E+05<br />

29bit fixed 1st order 40bit floating 1st order 40bit floating 2nd order<br />

33bit fixed 1st order<br />

Figure 3.9: Comparison of the different type of numbers <strong>and</strong> the different discretization<br />

The goal is to find the finest grid resolution for a given precision where the<br />

roundoff <strong>and</strong> truncation errors are in the same range. This can be easily determined<br />

by examining Figure 3.9, where the optimal grid size is located at the<br />

minimum point of the error function.<br />

The second goal is to find the optimal precision for a given grid resolution.<br />

Starting from a low precision simulation <strong>and</strong> increasing the precision by one<br />

bit, the error is halved in each step when the roundoff error is larger than the<br />

truncation error. The precision should be increased until the error term can not<br />

be decreasing any more. In this point the error of the solution will be dominated<br />

by the truncation error <strong>and</strong> the effect of roundoff error will be negligible.

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