## 3 Results
### Inference Energy Efficiency  
Neural network inference energy efficiency tests showed a much greater variety of results compared to previous tests, both in the context of the most and least effective product. The results of all the tests carried out are presented in Table 9, where, as in the previous section, the source data (inference time and power during inference) used to calculate the energy efficiency are also included. Measurements obtained for the PyTorch framework (Table 9: YOLO11 PyTorch and ResNet50 PyTorch) showed more than twice the power efficiency advantage of the Nvidia card over other solutions. AMD and Intel cards in these networks worked at much higher power while generating longer inference times. Tests of inference in the Tensorflow framework (Table 9: MobileNetV2 Tensorflow and ResNet50 Tensorflow), in turn, showed the high efficiency of the AMD card. All of the measured cards in this framework achieved a similar inference time, and none of the cards came close to the manufacturer's power limit (TGP). This indicates that the speed limit is a factor other than the speed of the graphics chip. In such a situation, the driver that manages the energy states [76] where the graphics card is located during inference has a greater impact on energy efficiency. The AMD graphics card was in the lowest energy state during the Tensorflow inference, which allowed for the highest power efficiency. A reference to the network models in the ONNX framework (Table 9: YOLO 11 ONNX and ResNet50 ONNX) showed the significantly higher power efficiency of the Nvidia card compared to other cards. Nvidia achieved the shortest inference times: 5.68 ms for YOLO 11 and 2.67 ms for ResNet50, with low power consumption: 103 W for YOLO 11 and 151 W for ResNet50. The AMD card achieved significantly longer inference times: 8.18 ms for YOLO 11 and 3.29 ms for ResNet50. In addition, the high power consumption during the inference, 248.33 W for YOLO 11 and 267.27 W for ResNet50, resulted in more than three times worse energy efficiency in the YOLO 11 test and more than twice as bad in the ResNet50 test compared to the Nvidia card. The Intel card worked with the lowest power consumption: 77.63 W for YOLO 11 and 80.86 W for ResNet50. At the same time, the longest inference times of 15.61 ms for YOLO 11 and 10.27 ms for ResNet50, respectively, resulted in lower power efficiency compared to Nvidia's card. Despite this, in the YOLO 11 test, the Intel card was 1.68 times more energy efficient than the AMD card. On the other hand, the ResNet50 network turned out to be 9.42 times less energy efficient than AMD. The OpenCV library does not support AMD HIP and ROCm, which requires the use of OpenCL to perform calculations on AMD graphics cards. This results in a significant \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**AMD**} & \multicolumn{2}{c}{**Intel**} & \multicolumn{2}{c}{**Nvidia**} & \multicolumn{2}{c}{**AMD**} & \multicolumn{2}{c}{**Intel**} & \multicolumn{1}{c}{**Nvidia**} \\ \cline{2-10}  & **T [ms]** & **P [W]** & **T [ms]** & **P [W]** & **T [ms]** & **P [W]** & **E [im/k]** & **E [im/k]** & **E [im/k]** \\ \hline YOLO 11 PyTorch & 8.42 & 243.43 & 19.99 & 106.17 & 6.62 & 118.00 & 487.98 & 471.25 & 1279.52 \\ \hline ResNet50 PyTorch & 5.10 & 193.44 & 11.41 & 97.57 & 4.01 & 84.92 & 1014.09 & 898.14 & 2938.52 \\ \hline MobileNetV2 Tensorflow & 36.91 & 36.16 & 40.74 & 56.28 & 38.88 & 60.34 & 749.19 & 436.11 & 426.21 \\ \hline ResNet50 Tensorflow & 40.13 & 32.98 & 44.68 & 60.60 & 41.29 & 46.54 & 755.70 & 369.31 & 520.43 \\ \hline YOLO 11 ONNX & 8.18 & 248.33 & 15.61 & 77.63 & 5.68 & 103.00 & 492.14 & 825.01 & 1710.56 \\ \hline ResNet50 ONNX & 3.29 & 267.27 & 10.27 & 80.86 & 2.67 & 151.73 & 1137.19 & 120.69 & 2468.07 \\ \hline YOLO v4 OpenCV & 605.62 & 235.14 & 110.08 & 80.52 & 34.59 & 313.50 & 7.22 & 112.81 & 92.22 \\ \hline EfficientNet B0 OpenCV & 274.75 & 98.42 & 3.35 & 71.66 & 1.48 & 100.00 & 36.98 & 4159.85 & 6773.51 \\ \hline \hline \end{tabular}
\end{table}