# Accelerating Image Classification using Feature Map Similarity in Convolutional Neural Networks
## 4 Experimental Results and Analysis
### Classification Time  
We measured the classification time with the cache on two low-performance devices: ODROID [25] and LattePanda [26]. Table 4 shows their specifications. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline
**Models** & \multicolumn{11}{c}{**Hit Rate (\%)**} \\ \hline AlexNet & 73.3 & 58.7 & 44.5 & 45.6 & 44.9 & & & & & & & & & & & \\ GoogLeNet (v1) & 71.1 & 67.6 & 49.8 & 42.0 & 34.1 & 36.8 & 39.0 & 31.6 & 31.0 & 31.6 & 28.4 & & & & & & \\ VGG-16 & 76.2 & 75.3 & 72.5 & 61.9 & 59.6 & 55.3 & 52.9 & 50.8 & 50.8 & 49.1 & 42.1 & 41.2 & 41.3 & & & & & & \\ VGG-19 & 77.3 & 73.7 & 73.1 & 62.1 & 59.8 & 55.4 & 51.4 & 53.7 & 51.7 & 49.7 & 51.5 & 49.6 & 43.8 & 42.9 & 41.3 & 41.1 & & & & & \\ ResNet-50 & 69.4 & 62.3 & 59.8 & 56.5 & 51.9 & 51.8 & 54.7 & 45.9 & 41.7 & 40.2 & 35.5 & 41.2 & 39.5 & 33.8 & 30.6 & 27.2 & 26.3 & & & & & & \\ ResNet-101 (1–17) & 64.5 & 53.7 & 52.0 & 49.3 & 44.6 & 42.8 & 41.3 & 39.3 & 38.1 & 39.0 & 41.0 & 40.8 & 38.9 & 37.2 & 37.7 & 37.5 & 35.4 & & & & & & & \\ ResNet-101 (18–34) & 35.9 & 34.9 & 34.5 & 33.9 & 32.5 & 33.4 & 34.2 & 32.0 & 29.6 & 29.0 & 30.2 & 29.1 & 28.0 & 28.0 & 22.2 & 19.8 & 21.6 & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hit rate when applying distance threshold. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline
**Models** & \multicolumn{11}{c}{**Precision (0 to 1)**} \\ \hline AlexNet & 0.39 & 0.57 & 0.67 & 0.66 & 0.71 & & & & & & & & & & & & & & \\  & **0.41** & **0.59** & **0.73** & **0.73** & **0.78** & & & & & & & & & & & & \\ GoogLeNet (v1) & 0.40 & 0.43 & 0.63 & 0.72 & 0.84 & 0.83 & 0.82 & 0.94 & 0.95 & 0.96 & 0.99 & & & & & & & & \\  & **0.41** & **0.44** & **0.67** & **0.79** & **0.91** & **0.89** & **0.89** & **0.99** & **0.99** & **1.00** & **1.00** & & & & & & & \\ VGG-16 & 0.30 & 0.37 & 0.40 & 0.49 & 0.51 & 0.54 & 0.61 & 0.62 & 0.63 & 0.70 & 0.77 & 0.78 & 0.85 & & & & & & & \\  & **0.30** & **0.36** & **0.40** & **0.50** & **0.53** & **0.57** & **0.64** & **0.65** & **0.67** & **0.74** & **0.79** & **0.82** & **0.88** & & & & & & & \\ VGG-19 & 0.29 & 0.37 & 0.41 & 0.49 & 0.52 & 0.59 & 0.64 & 0.64 & 0.65 & 0.68 & 0.65 & 0.67 & 0.76 & 0.78 & 0.82 & 0.86 & & & & & & & \\  & **0.29** & **0.35** & **0.41** & **0.51** & **0.54** & **0.61** & **0.67** & **0.69** & **0.69** & **0.72** & **0.68** & **0.70** & **0.80** & **0.83** & **0.86** & **0.88** & & & & & & & \\ ResNet-50 & 0.34 & 0.47 & 0.51 & 0.53 & 0.58 & 0.57 & 0.56 & 0.66 & 0.69 & 0.71 & 0.75 & 0.71 & 0.77 & 0.80 & 0.89 & 0.95 & 0.98 & 0.95 & 0.98 & 0.99 & 0.99 \\  & **0.36** & **0.49** & **0.51** & **0.54** & **0.61** & **0.60** & **0.61** & **0.69** & **0.73** & **0.77** & **0.81** & **0.76** & **0.84** & **0.87** & **0.91** & **0.99** & **1.00** \\ ResNet-101 (1–17) & 0.32 & 0.43 & 0.47 & 0.50 & 0.57 & 0.57 & 0.59 & 0.62 & 0.64 & 0.61 & 0.61 & 0.58 & 0.62 & 0.63 & 0.63 & 0.62 & 0.64 & 0.63 & 0.62 & 0.64 \\  & **0.34** & **0.45** & **0.48** & **0.50** & **0.61** & **0.59** & **0.61** & **0.64** & **0.68** & **0.65** & **0.62** & **0.60** & **0.65** & **0.65** & **0.65** & **0.64** & **0.67** \\ ResNet-101 (18–34) & 0.63 & 0.64 & 0.67 & 0.67 & 0.68 & 0.69 & 0.69 & 0.72 & 0.75 & 0.78 & 0.79 & 0.80 & 0.83 & 0.86 In these devices, the classification times required by a CNN without a cache, and with a cache at each convolution layer, were measured. Figure 7 shows the classification time required by the CNN and cache, and the hit rate of each device. The blue and green horizontal solid lines of each graph represent the classification times taken when one image was classified through CNN on each device. The blue and green dashed lines represent the classification times taken when classifying one image using the cache. The measured classification time required by the CNN is the computation time from the first convolution layer to the last located layer that outputs the probability for each class, until the classified class is returned.