Reconfigurable Hardware Implementation of a Multivariate Polynomial Interpolation Algorithm

Table 6

Experimental results for stage 2.

𝑛	Basis Vars	Class Reps	Slices∗	Slice FFs^∗	4-input LUTs^∗	FIFO16/ RAMB16^∗	𝑡𝐹 (ms)	𝑡𝐶 (ms)	Speedup
8	2	23	7745 (8.69%)	8335 (4.68%)	13846 (7.77%)	28 (8.33%)	0.10	1.59	16.67×
8	3	63	7950 (8.92%)	8352 (4.69%)	14205 (7.97%)	28 (8.33%)	0.44	7.76	17.68×
8	4	90	7955 (8.93%)	8359 (4.69%)	14213 (7.98%)	30 (8.93%)	0.84	16.60	19.71×
9	2	25	8520 (9.56%)	9220 (5.17%)	15205 (8.53%)	28 (8.33%)	0.10	1.85	17.64×
9	3	77	8539 (9.58%)	9226 (5.18%)	15239 (8.55%)	28 (8.33%)	0.69	11.20	16.13×
9	4	94	8582 (9.63%)	9260 (5.2%)	15340 (8.61%)	34 (10.12%)	1.14	20.26	17.81×
10	2	24	9188 (10.31%)	10098 (5.67%)	16710 (9.38%)	31 (9.23%)	0.10	1.78	17.95×
10	3	65	9192 (10.32%)	10106 (5.67%)	16714 (9.38%)	31 (9.23%)	0.47	8.30	17.69×
10	4	92	9201 (10.33%)	10130 (5.69%)	16742 (9.40%)	33 (9.82%)	0.86	18.43	21.36×
11	2	25	10130 (11.37%)	10978 (6.16%)	18024 (10.12%)	34 (10.12%)	0.11	2.01	18.80×
11	3	75	10156 (11.4%)	11001 (6.17%)	18067 (10.14%)	36 (10.71%)	0.58	12.80	22.22×
11	4	92	10115 (11.35%)	11008 (6.18%)	18025 (10.12%)	37 (11.01%)	1.05	23.00	21.93×
12	2	25	10861 (12.19%)	11861 (6.66%)	19077 (10.71%)	36 (10.71%)	0.10	2.05	19.93×
12	3	70	10806 (12.13%)	11881 (6.67%)	19012 (10.67%)	38 (11.31%)	0.52	10.68	20.41×
12	4	93	10877 (12.21%)	11895 (6.68%)	19121 (10.73%)	38 (11.31%)	1.03	19.90	19.26×
13	2	25	11405 (12.8%)	12747 (7.15%)	20240 (11.36%)	39 (11.61%)	0.10	2.13	20.74×
13	3	71	11469 (12.87%)	12760 (7.16%)	20358 (11.43%)	40 (11.90%)	0.53	12.50	23.68×
13	4	91	11416 (12.81%)	12771 (7.17%)	20274 (11.38%)	41 (12.20%)	0.85	21.38	25.21×

^∗Resource percentages are for a Virtex-4 XC4VLX200ff1513-11.