## 6 Conclusions  
In this paper, we presented an optimised implementation of a recurrent Spiking Neural Network on embedded microcontrollers for damage detection in Structural Health Monitoring applications. We studied the feasibility of spiking based processing and the trade-offs involved in using an event-based input. Thanks to this implementation, called eLSNN, we were able to design, implement and characterise an SNN-based monitoring system, where the computation is performed near to the sensor node. We described the optimisations we performed with respect to a naive LSNN algorithm present in the state-of-art to reduce computation cycles and improve energy efficiency. We also studied two alternative encodings of the input, showing how they impact performance and energy. We highlight the trade-offs between eLSNN execution and data transfer costs that have to be explored to select the best energy/performance configurations. Results of accuracy obtained on a real use case demonstrated that LSNN is a viable solution for damage detection in SHM, having high accuracy (MCC \(\geq\) 0.75) and low cycle and energy overheads if compared with the data transfer costs. Moreover, the results highlight that moving the spike-coding directly in the sensor can lead to even more energy-efficient implementation. The eLSNN working with input spikes is more energy-efficient than the one using real (continuous) values. The promising results in this work open the way to the implementation of SNN-based processing on-edge. Moreover, when compared with a state-of-art work leveraging an autoencoder (ANN) on the same dataset [7], the proposed eLSSN approach shows lower complexity (from 32,000 MAC operations to 15,750 sums and 750 multiplications).  
Future work will be devoted to evaluating and comparing event-based SNN approaches against other SHM datasets and comparing extensively with ANN algorithms.  
Conceptualization, F.B., L.Z., E.P., A.B. ([PERSON]) and A.A.; Methodology, F.B., L.Z. and E.P.; Software, F.B., L.Z. and E.P.; Supervision, A.B. ([PERSON]) and A.A.; Writing--original draft, F.B., L.Z., E.P., D.B., A.B. ([PERSON]) and A.A.; Writing--review & editing, F.B., L.Z., E.P., A.B. ([PERSON]), A.B. ([PERSON]) and A.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by INSIST Project (PON Ricerca e Innovazione 2014-2020)-Sistema di monitoraggio INtelligente per la Sicurezza delle InfraStrutture urbane grant number ARS01-00913.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Event-Driven**} \\ \cline{2-6}
**Stage** & **Time** & **Cycles** & **Current** & **Power** & **Energy** \\  & [\(\mu\)s] & [\(\#\)] & [\(mA\)] & [\(mW\)] & [\(\mu\)j] \\ \hline SPI & 949 & - & 4.33 & 14.29 & 13.56 \\ FFT & 101 & 16,968 & 38.17 & 125.96 & 12.72 \\ ABS & 50 & 8316 & 35.97 & 118.70 & 5.94 \\ Encoding & 32 & 5459 & 42.65 & 140.75 & 4.50 \\ eLSNN & 99 & 16,631 & 38.20 & 126.06 & 12.48 \\ \hline Total & 1231 & 47,374 & - & - & 49.20 \\ Mean & - & - & 12.11 & 39.97 & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Power report of the SHM application featuring the best eLSNNs tested. The SPI master transfers data with a serial clock of 8 MHz. The SPI stage includes: (i) The SPI data transfer, (ii) the time required by the system-on-chip to reconfigure the clock to 168 MHz when it wakes-up from SLEEP mode and (iii) the conversion of sensor data from raw integers to floating-point.