# Efficient FPGA Implementation of an ANN-Based Demapper Using Cross-Layer Analysis
## 6 Comparison
### ANN Solution vs. Conventional Systems  
Currently, the practical application of machine learning algorithms in the area of communication systems is fairly low. To analyze if there is potential of the ANN-based AE to replace highly-optimized traditional communication algorithms and to show pros and cons, we provide a short discussion in the following.  
Therefore, we implement the suboptimal soft-demapping algorithm proposed in [38] which replaces exponential and logarithmic functions by simplified calculations according to:  
\[llr(b_{k}|s_{r})=\frac{1}{2\sigma^{2}}\cdot\left\{\min_{i}(s_{r}-c_{i,k=0})^{2 }-\min_{i}(s_{r}-c_{i,k=1})^{2}\right\}\]  
where \(b_{k}\) is the \(k\)-th bit, \(s_{r}\) is the received symbol and \(c_{i,k=0}\) and \(c_{i,k=1}\) represent the constellation symbols whose \(k\)-th bits are '0' and '1', respectively. A comparison to the ANN-based demapper is given in Table 2.  
By comparing the conventional soft-demapper implementation to the AE-based solutions, it becomes clear that the implementation complexity of a conventional demapper on FPGA is much lower compared to a system based on ANNs. Nevertheless, the results show that the AE can achieve comparable performance when optimized for the respective metric. The latency-optimized AE implementation achieves a latency in the same order of magnitude as the conventional soft-demapper. The same applies to the power-optimized AE when comparing its power consumption to that of the soft-demapper. However, the throughput of the soft-demapper is much higher compared to the AE and it could even be increased further by instantiating multiple modules in parallel, which is possible due to the low resource utilization. This shows that FPGA implementations of ANN-based systems currently lag behind highly-optimized traditional systems in terms of implementation complexity when considering the demapping only. Nevertheless, various works demonstrated that an AE-based system is able to also perform much more complex tasks of the communication chain. For example, the authors of [39] showed that a small ANN of only two convolutional layers is able to perform blind channel equalization. Moreover, the authors of [40] demonstrated that even a complex Wiener filter can be outperformed by an ANN-based approach. Those results show that also more complex communication tasks can be fulfilled by an end-to-end AE system, which might result in a more competitive hardware implementation as further processing blocks are implicitly included in the AE. Additionally, end-to-end trainable systems have advantages in specific application scenarios for multiple reasons. To build a traditional communication system for a real-world scenario, it is essential to have a channel model that represents the real channel as close as possible. If there are large deviations between generic model and reality, the performance of the system suffers dramatically in certain conditions. In addition, even if the channel is known precisely, adjusting the parameters of the different communication blocks requires  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **BRAM** & **DSP** & **FF** & **LUT** & **Latency** & **Throughput** & **Power** \\   AE low-power objective & 6 & 3 & 4283 & 4248 & 1.36\(\cdot 10^{-6}\) & 2.94\(\cdot 10^{6}\) & 7.68\(\cdot 10^{-2}\) \\ AE low-latency objective & 18.5 & 352 & 10,895 & 11,343 & 8.10\(\cdot 10^{-8}\) & 4.92\(\cdot 10^{7}\) & 4.53\(\cdot 10^{-1}\) \\ Conventional demapper & 0 & 1 & 1042 & 1107 & 5.33\(\cdot 10^{-8}\) & 3.00\(\cdot 10^{8}\) & 5.5\(\cdot 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of AE-based inference to conventional soft demapping.  
much development time and the knowledge of a communication expert. In contrast, an end-to-end trainable system does not need detailed information about the underlying channel but is able to correct deviations of the model by fine-tuning during runtime. Moreover, an extensive parameter search is not necessary, as it is implicitly included in the training of the network.