# LSTM Accelerator for Convolutional Object Identification
## 4 Experiments and Results
### Evaluation  
Regarding the results as presented in Tables 4-7, as well as Figures 2 and 3, a safe assumption can be extracted concerning the fluctuations in accuracy of the concerned methods. More specifically, the performance of the proposed schemes in terms of accuracy, loss (cross-entropy), in accordance with execution time (in seconds) has been measured. All results are comprised of the average out of five different runs in order to avoid outliers. As expected, the baseline approach that contained a regular convolutional scheme maintained a steady and linear progression in its accuracy, as well as in the time needed for the completion of the experiments. In the event of including a batch normalization layer after the convolutional net, the total completion time of the corresponding experiments marginally increased. That chronic increment amounts to the sum of computations applied during the reduction of the covariate shift. Making the convolutional network deeper, thus more complex in dimensionality, will affect the model's performance. In the present work, convolutional networks of various depths were implemented in order to detect the version that could provide the better time-accuracy trade-off. Indeed, the more complex CNN managed to achieve higher percentages of accuracy. Despite the time needed, accuracy was increased in high ranks, which made the trade-off suboptimal. The conglomerate structure of the previous baseline network, in sequence with our additional LSTM neural net logic, yielded results within our expected range of values. The precision of our novel approach scaled increasingly as the model was trained with additional epochs. In comparison with the baseline methods, similar results were achieved with a standard deviation of \(2\times 10^{-3}\), while also occasionally yielding surpassing accuracy scores. However, on the contrary, in terms of the experiment completion time, the results highlighted our approach as importantly more rapid in execution. While maintaining similar amounts of accuracy, our novel CNN-LSTM technique managed to reduce the execution time by 30% during the first unstable epochs and by approximately 42% after convergence. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Dimensions** & **Channels** & **Kernel Size** \\ \hline Input Image & \(28\times 28\) & \(1\) & \\ Convolutional/ReLU & \(28\times 28\) & \(32\) & \(5\times 5\) \\ Max Pooling Layer & \(14\times 14\) & \(32\) & \(2\times 2\) \\ Batch Normalization & \(14\times 14\) & \(32\) & \\ Convolutional/ReLU & \(14\times 14\) & \(64\) & \(3\times 3\) \\ Max Pooling Layer & \(7\times 7\) & \(64\) & \(2\times 2\) \\ Batch Normalization & \(7\times 7\) & \(64\) & \\ LSTM Layer & \(7\times 1\) & \(64\) & \\ Fully-Connected/Dense & & \(1000\) & \\ Dropout Layer & & & \\ Fully-Connected/Softmax & & \(10\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Abbreviations for different methods utilized. More specifically, after 50 epochs of training on the MNIST dataset, our model achieved a 36% and 44% improvement on its results on the 2-CNN model and 3-CNN model, respectively; that is, regarding the comparison of the model that contained our contribution with the CNN-LSTM architecture against the model that achieved the highest accuracy. With the gradual increase of the data that were supplied for the training step of the models, their percentages were scaled to 42% and 40%, again, respectively. Regarding the experiments on the CIFAR10 database, our model provided similar results in terms of accuracy and execution time, as expected. The first results of the execution time comparison presented a 42% improvement on both the 2-CNN, as well as 3-CNN layer models. Providing a bigger amount of data, just like the previous experiment with MNIST, yielded an improvement of 41% and 43% accordingly.