# MLoF: Machine Learning Accelerators for the Low-Cost FPGA Platforms
## 5 Experimental Analysis and Result  
To evaluate the performance of these IPs, we select six typical IoT endpoint datasets for different parameter combinations and tests. As shown in Table 6, the datasets include binary classifications, multi-classifications, and regressions. The Gutter Oil dataset proposed by VeriMake Innovation Lab aims to detect gutter oils [31], and contains six input oil features, including the pH value, refractive index, peroxide value, conductivity, pH value differences under different temperatures, and conductivity value difference under different temperatures. This dataset can serve both in a dichotomous and a polytomous way. The Smart Grid dataset for conducting research on electrical grid stability is from Karlsruher Institut fur Technologie, Germany [32; 33]. This is a dichotomous dataset with 13 input features used to determine whether the grid is stable under different loads. The Wine Quality dataset is proposed by the University of Minho [34] for classifying wine quality. This is a polytomous dataset, with 11 input dimensions (e.g., humidity, light, etc. ), rating wines on a scale of 0 to 10. The rain dataset by the Bureau of Meteorology, Australia, is based on datasets of different weather stations for recording and forecasting the weather [35]. This is a dataset for regression prediction, using eight input parameters, such as wind, humidity, and light intensity to predict the probability of rain. Power Consumption is an open-source dataset created by the University of California, Irvine. It tracks the total energy consumption of various devices within families [36]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Producer** & **Device** & **Manufacturer** & **Board Name** & **Memory** & **Interface** & **Price** \\ \hline Microchip & M2S010 & Trenz electronic & SMF2000 & SDRAM, SPI Flash & / & \$59.9 \\ \hline Pango & PGL12G & ALINX & \begin{tabular}{c} PGL12G \\ Development \\ Board \\ \end{tabular} & \begin{tabular}{c} SDRAM, SPI \\ Flash \\ \end{tabular} & \begin{tabular}{c} ADC, HDMI, \\ MIPI \\ \end{tabular} & \$52 \\ \hline Xilinx & Artix-7 & QMTECH & \
\begin{tabular}{c} Artix-7 \\ Development \\ Board \\ \end{tabular} &
\begin{tabular}{c} SDRAM, SPI \\ Flash \\ \end{tabular} & ADC, MIPI & \$79.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Producer** & **EDA Software** & **Synthesis Tool** & **Availability** \\ \hline Anlogic & Tang Dynasty & TD Integrated Synthesis & Commercial \\ Gowin & GOWIN EDA & GowinSynthesis/Splify & Commercial \\ Intel & Quartus Prime & Quartus Integrated & Free License/Commercial \\ Lattice & iCEcube2 & Synplify Pro & Free License/Commercial \\ Lattice & Lattice Diamond & Lattice Synthesis Engine & Free License/Commercial \\ Microchip & Libero SoC & Synplify Pro ME & Commercial \\ Pango & Pango Design Suite & ADS/Synplify & Commercial \\ Xilinx & Vivado & Xilinx Synthesis & Free License/Commercial \\ Technology & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The specification of EDA software. We use a desktop PC with a 2.59 GHz Core i7 processor to train various models on the six datasets and export the best parameters with the best scores obtained during training. For binary classifications and multiclass classifications, the scores represent the classification accuracies. For linear regressions, the scores represent R2 [37]. Then, these trained parameters are fed to our machine learning IPs and implemented on 10 different FPGA boards using EDAs from seven different candidate producers. Each EDA is configured to operate in the balanced mode with identical constraints. As shown in Table 5, part of the EDAs is integrated with synthesis tools, such as Gowin and Pango. However, as Synplify requires an individual supporting license, in this paper, only their self-developed synthesis tools (GowinSynthesis, ADS) are used for analyzing FPGA implementations.