# MLoF: Machine Learning Accelerators for the Low-Cost FPGA Platforms
## Abbreviations
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Device** & **LUTs** & **DSPs** & **Latency** & **Power** & **PLP** & **CPLP** \\ \hline \multirow{8}{*}{Gutter Oil} & EF2M45 & 1251 & 15 & 17.26 & 446.36 & 7703.32 & 199,515.87 \\  & EG4S20 & 942 & 18 & 16.08 & 315.81 & 5077.64 & 253,374.31 \\  & GW2A & 991 & 6 & 5.18 & 397.25 & 2059.34 & 348,029.14 \\  & Cyclone10 LP10 CL & 984 & 6 & 10.63 & 282.49 & 3001.74 & 149,786.76 \\  & MAX10M50 DAF & 985 & 6 & 9.33 & 335.91 & 3135.05 & 266,479.08 \\  & ICE40 UP5 & 979 & 6 & 7.81 & 134 & 1046.54 & 39,768.52 \\  & MachXO2 & 1062 & / & 8.84 & 185.47 & 1638.63 & 48,994.96 \\  & M2S010 & 990 & 6 & 13.32 & 325.57 & 4337.50 & 259,816.40 \\  & PGL12G & / & / & / & / & / \\  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The k-NN deployment results on the low-cost FPGAs. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Device** & **LUTs** & **DSPs** & **Latency** & **Power** & **PLP** & **CPLP** \\ \hline \multirow{8}{*}{Gutter Oil} & EF2M45 & 1361 & 15 & 16.92 & 452.82 & 7663.01 & 198,471.83 \\  & EG4S20 & 1058 & 18 & 16.62 & 331.42 & 5507.27 & 274,812.90 \\  & GW2A & 1047 & 6 & 5.18 & 406.45 & 2107.04 & 356,089.22 \\  & Cyclone10 LP10 CL & 1076 & 6 & 10.63 & 282.79 & 3005.77 & 149,988.17 \\  & MAX10M50 DAF & 1088 & 6 & 8.83 & 335.66 & 2964.55 & 251,986.68 \\  & IC240 UP5 & 1077 & 6 & 7.81 & 135 & 1054.35 & 40,065.30 \\  & MachXO2 & 1241 & / & 7.62 & 185.47 & 1414.02 & 42,279.30 \\  & M2S010 & 1117 & 6 & 12.94 & 325.72 & 4213.81 & 252,407.44 \\  & PGL12G & / & / & / & / & / & / \\  & Artix-7 & 673 & 18 & 11.57 & 597 & 6907.29 & 551,892.47 \\ \hline \multirow{8}{*}{Wine Quality} & EF2M45 & / & / & / & / & / \\  & EG4S20 & 4435 & 29 & 18.96 & 672.69 & 12,752.20 & 636,334.94 \\  & GW2A & 3415 & 11 & 6.46 & 578.87 & 3739.51 & 631,977.72 \\  & Cyclone10 LP10 CL & 3073 & 11 & 11.10 & 194.79 & 2162.56 & 107,911.67 \\  & MAX10M50 DAF & 3067 & 11 & 9.27 & 354 & 3280.87 & 278,874.12 \\  & ICE40 UP5 & 3848 & 8 & 9.13 & 135 & 1232.42 & 46,831.77 \\  & MachXO2 & 4348 & / & 7.18 & 186 & 1335.67 & 39,936.41 \\  & M2S010 & 3568 & 11 & 13.35 & 235.65 & 3145.95 & 188,442.66 \\  & PGL12G & / & / & / & / & / & / \\  & Artix-7 & 2037 & 33 & 12.10 & 511 & 6183.10 & 494,029.69 \\ \hline \multirow{8}{*}{Rain} & EF2M45 & / & / & / & / & / & / \\  & EG4S20 & 11,767 & 24 & 18 & 1491.21 & 26,843.27 & 1,339,479.23 \\  & GW2A & 6297 & 8 & 7.76 & 886.53 & 6882.98 & 1,163,223.64 \\  & Cyclone10 LP10 CL & 5397 & 8 & 11.89 & 305.54 & 3633.79 & 181,325.98 \\  & MAX10M50 DAF & 5412 & 8 & 10.09 & 362.04 & 3651.17 & 310,349.74 \\  & ICE40 UP5 & / & / & / & / & / & / \\  & MachXO2 & / & / & / & / & / & / \\  & M2S010 & 6218 & 8 & 12.94 & 471.37 & 6097.62 & 365,247.23 \\  & PGL12G & / & / & / & / & / & / \\  & AF2M45 & 1735 & 15 & 17.75 & 485.83 & 8625.01 & 223,387.78 \\  & EG4S20 & 1071 & 21 & 15.30 & 365.83 & 5598.36 & 279,358.05 \\  & GW2A & 1095 & 7 & 5.18 & 321.68 & 1667.57 & 281,819.93 \\  & Cyclone10 LP10 CL & 1160 & 7 & 9.07 & 288.61 & 2617.40 & 130,608.46 \\  & MAX10M50 DAF & 1173 & 7 & 10.54 & 339.08 & 3573.90 & 303,781.77 \\  & ICE40 UP5 & 1159 & 7 & 9.36 & 127 & 1188.21 & 45,152.06 \\  & MachXO2 & 1864 & / & 8.81 & 195.09 & 1719.12 & 51,401.82 \\  & M2S010 & 1232 & 7 & 11.86 & 212.88 & 2524.48 & 151,216.63 \\  & PCI12G & / & / & / & / & / & / \\  & Artix-7 & 734 & 21 & 11.95 & 534 & 6381.30 & 509,665.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._