# Configurable Hardware Core for IoT Object Detection
## 2 Background and Related Work
### Tiny YOLOv3
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer \#** & **Type** & \begin{tabular}{c} **Input** \\ **(W \(\times\) H \(\times\) C)** \\ \end{tabular} & \
\begin{tabular}{c} **Output** \\ **(V \(\times\) U \(\times\) N)** \\ \end{tabular} &
\begin{tabular}{c} **Kernel** \\ **(N \(\times\) (I \(\times\) K \(\times\) C))** \\ \end{tabular} & **Activation** \\ \hline
1 & Conv. & \(416\times 416\times 3\) & \(416\times 416\times 16\) & \(16\times(3\times 3\times 3)\) & Leaky \\
2 & Maxpool & \(416\times 416\times 16\) & \(208\times 208\times 16\) & \\
3 & Conv. & \(208\times 208\times 16\) & \(208\times 208\times 32\) & \(32\times(3\times 3\times 16)\) & Leaky \\
4 & Maxpool & \(208\times 208\times 32\) & \(104\times 104\times 32\) & \\
5 & Conv. & \(104\times 104\times 32\) & \(104\times 104\times 64\) & \(64\times(3\times 3\times 32)\) & Leaky \\
6 & Maxpool & \(104\times 104\times 64\) & \(52\times 52\times 64\) & \\
7 & Conv. & \(52\times 52\times 64\) & \(52\times 52\times 128\) & \(128\times(3\times 3\times 64)\) & Leaky \\
8 & Maxpool & \(52\times 52\times 128\) & \(26\times 26\times 128\) & \\
9 & Conv. & \(26\times 26\times 128\) & \(26\times 26\times 256\) & \(256\times(3\times 3\times 128)\) & Leaky \\
10 & Maxpool & \(26\times 26\times 256\) & \(13\times 13\times 256\) & \\
11 & Conv. & \(13\times 13\times 256\) & \(13\times 13\times 512\) & \(512\times(3\times 3\times 256)\) & Leaky \\
12 & Maxpool & \(13\times 13\times 512\) & \(13\times 13\times 512\) & \\
13 & Conv. & \(13\times 13\times 512\) & \(13\times 13\times 1024\) & \(1024\times(3\times 3\times 512)\) & Leaky \\
14 & Conv. & \(13\times 13\times 1024\) & \(13\times 13\times 256\) & \(256\times(1\times 1\times 1024)\) & Leaky \\
15 & Conv. & \(13\times 13\times 256\) & \(13\times 13\times 512\) & \(512\times(3\times 3\times 256)\) & Leaky \\
16 & Conv. & \(13\times 13\times 512\) & \(13\times 13\times 255\) & \(255\times(1\times 1\times 512)\) & Linear \\
17 & Yolo & \(13\times 13\times 255\) & \(13\times 13\times 255\) & & Sigmoid \\
18 & Route & Layer 14 & \(13\times 13\times 256\) & \(13\times 13\times 128\) & \(128\times(1\times 1\times 256)\) & Leaky \\
20 & Upsample & \(13\times 13\times 128\) & \(26\times 26\times 128\) & & \\
21 & Route & Layer 9 + 20 & \(26\times 26\times 384\) & & \\
22 & Conv. & \(26\times 26\times 384\) & \(26\times 26\times 256\) & \(256\times(3\times 3\times 384)\) & Leaky \\
23 & Conv. & \(26\times 26\times 256\) & \(26\times 26\times 255\) & \(255\times(1\times 1\times 256)\) & Linear \\
24 & Yolo & \(26\times 26\times 255\) & \(26\times 26\times 255\) & & Sigmoid \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Tiny-YOLOv3 layers. Figure 1: Tiny YOLOv3 layer diagram. The object detection and classification part of the network performs object detection and classification at (\(13\times 13\)) and (\(26\times 26\)) grid scales. The detection at a lower resolution is obtained by passing the feature extraction output over \(3\times 3\) and \(1\times 1\) convolutional layers and a YOLO layer at the end. The detection at the higher resolution follows the same procedure but uses FMs from two layers of the network. The second detection uses intermediate results from the feature extraction layers concatenated with upscaled FMs used for the lower resolution detection. The combination of FMs from two different resolutions contributes to more meaningfulness using the information from the upsampled layer and the finer-grained information from the earlier feature maps [15].