# AxP: A HW-SW Co-Design Pipeline for Energy-Efficient Approximated ConvNets via Associative Matching
## 4 Experimental Results  
### Benchmarks and Datasets  
Table 1 reports the benchmarks adopted. Specifically, the dataset and ConvNet employed for each task, the input data size, and the detailed model topology. It also collects the baseline accuracy for the full-precision model (FP32) and the half-precision one (FP16). A detailed description of the tasks follows.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Task** & **IC** & **OR** & **KS** \\ \hline Dataset & MNIST [48] & GTSRBD [49] & GSC [27] \\ Model & LeNet-Like [45] & LeNet-5 [50] & GscNet [27] \\ \hline Input & \multicolumn{2}{c}{\(1\times 32\times 32\)} & \multicolumn{2}{c}{\(3\times 32\times 32\)} & \multicolumn{2}{c}{\(1\times 44\times 44\)} \\  & Conv & \(6\times 5\times 5\) & Conv & \(6\times 5\times 5\) & Conv & \(20\times 5\times 5\) \\  & MaxPool & \((2,2,2)\) & MaxPool & \((2,2,2)\) & MaxPool & \((2,2,1)\) \\  & Conv & \(16\times 5\times 5\) & Conv & \(16\times 5\times 5\) & Conv & \(20\times 5\times 5\) \\ Topology & MaxPool & \((2,2,2)\) & MaxPool & \((2,2,2)\) & MaxPool & \((2,2,1)\) \\  & Conv & \(120\times 5\times 5\) & Conv & \(120\times 5\times 5\) & FC & \(16\),\(280\times 1000\) \\  & FC & \(120\times 10\) & Conv & \(120\times 84\) & FC & \(1000\times 31\) \\  & & FC & \(84\times 43\) & & \\ \hline \multirow{2}{*}{Acc. Top-1 (\%)} & FP32 & \multirow{2}{*}{\begin{tabular}{c} 98.82 \\ FP16 \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} 87.13 \\ 98.67 \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} 69.41 \\ 87.10 \\ \end{tabular} } \\  & & & & \\ \end{tabular}
\end{table}
Table 1: Benchmark overview. Convolutional layers with shape \((ch_{b}\times kh\times kw)\), fully-connected layers with shape \((ch_{l}\times ch_{b})\) and pooling layers with shape \((kh,kw,s)\); \(kh\) and \(kw\) are kernel height and width, \(s\) is the stride, \(ch_{l}\) and \(ch_{b}\) refer to the number of input and output channels, respectively.  
**Image Classification (IC)**: the goal is to classify 10 different handwritten digits. The dataset employed is the popular MNIST [48] consisting of 60 k 32 \(\times\) 32 gray-scale images, 50k samples used for training, 10k for testing. The model employed is inspired by the LeNet [50] architecture widely adopted in previous works.  
**Object Recognition (OR)**: the objective is to recognize 43 different traffic signals, a popular sub-task for autonomous driving. The dataset adopted is the German Traffic Sign Recognition Dataset (GTSRD) [49]. It collects 50 K samples of 32 \(\times\) 32 RGB samples of traffic signals spread in the German streets, 40 k used for training, 10k testing. For this task, the ConvNet architecture is the LeNet-5 [50] model.  
**Keyword Spotting (KS)**: the objective is to recognize 30 simple vocal commands. The adopted dataset comes from Google research, Google Speech Commands (GSC) [27], with 65k one-second-long audio samples of 30 different keywords plus noise, labeled as \"unknown\". The inputs are the 2D spectrograms of the recorded samples, 56,196 samples are for training, 7518 for testing. The model adopted is the GSCNet taken from an open-source repository [51],which is inspired by the original work [27].