# Parallel Extreme Learning Machines Based on Frequency Multiplexing
## 4 Results  
### Parallel Operations Mode  
To test the ability of the system to run multiple tasks at the same time, the experiment was executed using only \(N_{S}=2\) input light sources out of the three available ones (wavelengths \(\lambda_{1}\) and \(\lambda_{2}\)). We tested the performances of the system on three classification tasks: iris classification [27], wine classification [28], and banknote authentication [29]. The iris classification task consists in selecting the correct class among three different ones, given a set of four different input features; the wine classification consists in selecting the correct class among three different ones, given a set of thirteen different input features; the banknote authentication task consists in selecting the correct class among two different ones, given a set of five different input features.  
In Section 3.3, we discussed the convenience of encoding the same input feature to \(d\) consecutive comb lines. The best performing \(d\) values for each task were established based both on simulations and on our previous experiments [19]. For each of the tasks, we tested the two best performing \(d\) values at the same time, running two tasks in parallel, each one on a different comb. Note that in so doing, the two parallel ELMs were always running the same task type (at a given time, both were running either iris classification or wine classification or banknote authentication): this was performed as such with the sole purpose of assuring the same execution times for both ELMs; if we ran two different task types, one of the two executions would have terminated before the other since each task has a different number of database entries. This was an experimental artifice employed to test the system in a situation of full parallelism, but the ELMs are completely independent and during normal operation they are not required to finish computation at the same time. One ELM could even start a new computation while another parallel ELM is still executing a different task. Note that even when the two parallel ELMs were running the same task type, the input layers injected into the ELMs were different (since the \(d\) values were different); this implies that the hidden layers and the optimal output weights were different as well, and that the two ELMs were indeed processing different data. The results are reported in Figure 3 and are compared with a simulation and with performances obtained by our previous experiment that featured only one light source and no parallelism [19]. Each datapoint represents the average of the scores obtained by testing 100 different random repartitions of the training and testing set; the error bars represent the standard deviation of such distribution of scores.  
Results clearly show that parallel operations are possible. We observed a slight decrease in accuracy when comparing the current performances with that of the previous single-comb experiment. This is likely due to the fact that the current experiment suffers from a decreased SNR, caused by power losses in the coupling between different sources, which then leads to less powerful combs.