# Trade-Off Analysis of Hardware Architectures for Channel-Quality Classification Models
## 5 Results
### Comparison with Other Works  
As far as we know, there are no hardware implementations for the channel-quality classification model. Nevertheless, we compare our hardware implementations with previous works that have proposed machine learning algorithms for hardware implementations similar to our proposals in terms of the resources mentioned before. We remark that the comparisons are not 100% fair since the implementations aim for different objectives using different datasets with variable sizes, but we contrast the results to highlight our results achieved. The four FPGA architectures that implemented the ML algorithms in this work (Decision Trees, NNs, Logistic Regression, and SVM) are compared with other implementations, see Table 4. Our work seeks to classify low and high CQI. In contrast, [PERSON] et al. [25] developed a Decision Tree aiming at large datasets, and [PERSON] et al. [26] presented a Decision Tree with dynamic training, applied to five binary datasets. [PERSON] et al. [27] developed an FFNN for estimating vertical forces on the four wheels of the electric car. [PERSON] et al. [28] implemented a Logistic Regression architecture, which was incorporated into a framework for data analytics called Spark. [PERSON] et al. [29] proposed an SVM classifier for speech recognition. [PERSON] et al. [30] presented an SVM architecture for matrix computing with changeable dimensions. Figure 13: Trade-Off between Accuracy and Efficiency. The present work uses balanced accuracy for evaluating the results; [PERSON] et al. [25] reported five different results since they evaluated the design using the accuracy metric over five data sets: (i) Bank Marketing, (ii) MAGIC Gamma Telescope, (iii) Australian New South Wales Electricity Market, (iv) Covertype, and (v) Person Activity. [PERSON] et al. [26] proved their architecture on five binary balanced datasets: (i) skin, (ii) occupancy, (iii) activity recognition, (iv) activity recognition, and (v) mammography. Their accuracy results correspond to these datasets. [PERSON] et al. [27] used the Mean Absolute Error metric to evaluate three different FFNNs configurations; [PERSON] et al. [28] and [PERSON] et al. [29] evaluated their works using the accuracy metric; [PERSON] et al. [30] chose the effective utilization rate for evaluation. Two decision trees with depths of five and six were implemented in our work, whereas [PERSON] et al. [25] developed one architecture, but they implemented it on five different datasets. [PERSON] et al. [26] developed one architecture, which was applied to five different datasets, obtaining the highest number of LUTs and FFs in this last work with 894,000 and 6488, respectively, followed by [PERSON] et al. [25] with a minimum number of LUTs of 54,198; however, they did not report the number of FFs. Our work obtained the minimum number of LUTs and FFs, where the Decision Tree with a depth of 5 has 320 and 812, respectively, and the Decision Tree with a depth of 6 has 890 and 1389, respectively. In the case of BRAMs, the maximum number was obtained by [PERSON] et al. [25] with 384, followed by [PERSON] et al. [26] with 228, which also has the highest number of DSPs with 200, followed by [PERSON] et al. [25] with 138. In contrast, our work did not make use of any BRAM or DSP. The reported latency in our work is given in clock cycles (23 and 25) and milliseconds (ms), in contrast, [PERSON] et al. [25] with 0.26 and [PERSON] et al. [26] with 24. The throughput and efficiency of our work achieve the highest frequency (MHz) with 323.31, followed by [PERSON] et al. [25] with 308 and by [PERSON] et al. [26] with 62. For Neural Networks, we presented an MLP model. In contrast, [PERSON] et al. [27] developed three FFNN models varying data type, activation function, and the number of iterations and obtaining three different results. We obtained a higher number of LUTs and DSPs, with 36,825 and 439, respectively, in contrast to [PERSON] et al. [27], which obtained 8881 LUTs and 14 DSPs, considering their best results.