## 7 Experimental Results
### Deployment on Smart Glasses
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Type** & **Optimizations** & \
\begin{tabular}{c} **Flash** \\ **(Available: 77,604 B)** \\ \end{tabular} &
\begin{tabular}{c} **RAM** \\ **(Available: 40,572 B)** \\ \end{tabular} & **Accuracy** \\ \hline \hline \multicolumn{5}{c}{32 filters} \\ \hline int8 & CMSIS-NN & 17,776 B & 20,680 B & 77.90\% \\ int8 & None & 17,216 B & 6,664 B & 77.90\% \\ int16 & CMSIS-NN & 31,440 B & 26,192 B & 79.21\% \\ int16 & None & 32,720 B & 13,328 B & 79.21\% \\ float32 & N/A & 60,336 B & 23,200 B & 79.28\% \\ \hline \multicolumn{5}{c}{48 filters} \\ \hline int16 & CMSIS-NN & 65,736 B & 38,512 B & 79.79\% \\ float32 & N/A & 128,952 B * & 33,440 B & 79.87\% \\ \hline \hline \end{tabular}  
* Memory overflow.  
\end{table} 
Table 2: Flash usage and static RAM allocation of the deep neural network (code and data). Inference time and energy measurements were collected for various configurations and are shown in Table 3.  
Results show that quantization also helps to reduce inference time and therefore energy consumption for one inference. The original 32-bit floating-point network requires 140 ms on average for one inference, while its 16-bit quantized version only takes 88 ms for the same accuracy. Furthermore, the 8-bit quantized version only requires 53 ms, but as seen previously with a noticeable degradation of accuracy. However, the overall energy consumption over one minute does not significantly change with quantization. The overall energy is reduced by at most 7% between the 32-bit floating-point network and its 8-bit quantized version. As it has been observed in Figure 9, the inference time is indeed small compared to the time required to collect data. For that reason, the impact of inference over the overall energy consumption is small. Therefore, even if the largest network that fits in memory (48 filters per convolution with 16-bit quantization) is used, the autonomy of the smart glasses would not be impacted as long as the inference execution time remains small compared to the inference period. Hence, the energy consumption over one minute only \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Type** & **Optimization** & **Inference Time** & **Energy for One Inference** & **Energy over One Minute** \\ \hline  & & & 32 filters & \\ \hline int8 & CMSIS-NN & 53 ms & 387 nWh & 220 \(\mu\)Wh \\ int8 & None & 115 ms & 722 nWh & 231 \(\mu\)Wh \\ int16 & CMSIS-NN & 88 ms & 605 nWh & 232 \(\mu\)Wh \\ int16 & None & 130 ms & 853 nWh & 234 \(\mu\)Wh \\ float32 & N/A & 140 ms & 919 nWh & 235 \(\mu\)Wh \\ \hline  & & & 48 filters & \\ \hline int16 & CMSIS-NN & 173 ms & 1120 nWh & 237 \(\mu\)Wh \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Inference time and energy measurements on the smart glasses  
Figure 9: Current and voltage captures over one inference period by Quitech Otii software for int16 model with 48 filters per convolution and CMSIS-NN optimizations  
grows by 2% with a 16-bit quantized network with 48 filters per convolution rather than using 32 filters per convolution. Ellcie Healthy's smart glasses embed a 350 mWh battery. Therefore, when the 16-bit quantized network with 48 filters per convolution is used (this network consumes 237 mWh per minute), the autonomy can reach 1476 min, i.e., 24.6 h. This estimated lifetime does not take into account additional applications that could run concurrently as well as battery ageing. The larger the neural network, the larger the memory and the higher the energy consumption. However, in our case study, the memory footprint is a far more important parameter than energy consumption, primarily making the artificial intelligence in the smart glasses a memory bound problem.